how to search for research articles in pubmed

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PubMed User Guide

Last update: February 14, 2024

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How can I get the full text article ? What if the link to the full text is not working ?
How do I search by author ?
How do I search by journal name ?
How do I find a specific citation ? I have some information such as the author, journal name, and publication year.
I retrieved too many citations. How can I focus my search ?
I retrieved too few citations. How can I expand my search ?
How do I find consumer health information about a disease or condition?
How do I find systematic reviews ?
Are there tools to help with clinical searches or finding medical genetics information?
I’m not finding what I need. How does a PubMed search work ?
Can you explain what is shown on the search results ?
How do I display an abstract ?
How can I save my results ?
Can I receive email updates when new results are available for my search ?
How do I report an error or duplicate citation in PubMed?
How can I cite an article or export citations to my citation management software program ?
How do I get a link to bookmark or share my PubMed search ?
How can I download PubMed?
Is there a guide to NLM resources for MEDLINE/PubMed ?
Where can I find further assistance and training ?

Search PubMed

How do i search pubmed, i retrieved too many citations. how can i focus my search, i retrieved too few citations. how can i expand my search, find a specific citation, searching by author, searching by journal, searching by date, searching for a phrase, truncating search terms, combining search terms with boolean operators (and, or, not), using search field tags, proximity searching.

Identify the key concepts for your search.
Enter the terms (or key concepts) in the search box.
Press the Enter key or click Search.

For many searches, it is not necessary to use special tags or syntax. PubMed uses multiple tools to help you find relevant results:

Best Match sort order uses a state-of-the-art machine learning algorithm to place the most relevant citations at the top of your results.
An autocomplete feature displays suggestions as you type your search terms. This feature is based on PubMed query log analysis described in " Finding Query Suggestions for PubMed ."
A spell checking feature suggests alternative spellings for search terms that may include misspellings.
A citation sensor displays suggested results for searches that include terms characteristic of citation searching, e.g., author names, journal titles, publication dates, and article titles.

To limit the number of search results:

Replace general search terms with more specific ones (e.g., low back pain instead of back pain).
Include additional terms in your query.
Use the sidebar filters to restrict results by publication date, full text availability, article type, and more.
On the abstract page for a citation, see the Similar Articles section for a pre-calculated set of additional PubMed citations closely related to that article.
Remove extraneous or specific terms from the search box.
Try using alternative terms to describe the concepts you are searching.

Paste the article title into the search box, or enter citation details such as the author, journal name and the year the article was published in the search box and the PubMed citation sensor will automatically analyze your query for citation information to return the correct citation. The citation sensor incorporates a fuzzy matching algorithm and will retrieve the best match even if a search includes an incorrect term. You do not need to use field tags or Boolean operators.

Enter the author’s last name and initials without punctuation in the search box, and click Search.

If you only know the author’s last name, use the author search field tag [au], e.g., brody[au].

Names entered using either the lastname+initials format (e.g., smith ja) or the full name format (john a smith) and no search tag are searched as authors as well as collaborators, if they exist in PubMed.

Enter a full author name in natural or inverted order, e.g., julia s wong or wong julia s.
Prior to 2002, full author names were not included on PubMed citations, so full author name searches will only retrieve citations from 2002 forward, when the full author name was published in the article.
A comma following the last name for searching is optional. For some names, however, it is necessary to distinguish which name is the last name by using the comma following the last name, e.g., james, ryan.
Omit periods after initials and put all suffixes at the end, e.g., vollmer charles jr

Initials and suffixes are not required. If you include a middle initial or suffix, you will only retrieve citations for articles that were published using the middle initial or suffix.

More information about author searching:

To search by author using the search builder, click Advanced search and then select Author from the All Fields menu. The author search box includes an autocomplete feature.
You may click an author link on the abstract display to execute a search for the author in PubMed. Results will display using a ranking algorithm if the author name is computationally similar for additional PubMed citations.
If an author name includes only stopwords , use the author search field tag [au] to search in combination with other terms, e.g., just by[au] seizure.
Author names are automatically truncated to account for varying initials and designations such as Jr. To turn off the truncation, use double quotes around the author's name with the author search field tag [au], e.g., "smith j"[au].
Use the search field tag [1au] to search for the first personal author or [lastau] to search for the last personal author name in a citation.

For additional information on author names in PubMed, please see the journal article, " Author Name Disambiguation for PubMed ."

Enter one of the following in the search box:

full journal title (e.g., molecular biology of the cell)
title abbreviation (e.g., mol biol cell)
ISSN number, a standardized international code (e.g., 1059-1524)

More information about journal searching:

To search by journal using the search builder, click Advanced search and then select Journal from the All Fields menu. The journal search box includes an autocomplete feature.
Click Journals in NCBI Databases on the PubMed homepage.
Enter the journal name and click Search.
Use the journal search field tag [ta] to limit your search to the journal only, e.g., gene therapy[ta], scanning[ta]
Searching with the full journal title or abbreviation is recommended for complete retrieval of indexed items; older citations may not have an ISSN.
If a journal title or abbreviation includes a special character (e.g., parentheses, brackets, &), enter the title or abbreviation without the special characters. For example, to search by the journal abbreviation j hand surg [am], enter j hand surg am.
Searching for a journal will automatically map to the official journal title and the title associated with an alternative title, if one exists. To turn off this automatic mapping enter the journal in double quotes and tag with [ta], e.g., "science"[ta].

A list of journals included in PubMed is available by FTP.

Using the results timeline

Using the search builder, searching by a single date in the search box, searching for a date range in the search box, searching for a relative date range.

Click and drag the sliders on the Results By Year timeline to change the date range for your search.

Note: The Results By Year timeline counts all publication dates for a citation as supplied by the publisher, e.g., print and electronic publication dates. These dates may span more than one year; for example, an article that was published online in November 2018 and published in a print issue in January 2019. This means the sum of results represented in the timeline may differ from the search results count.

Click Advanced search and use the search builder.
Select a date field from the All Fields menu, e.g., Date – Publication, and enter a single date or a date range in the fill-in-the-blank boxes. Month and day are optional. If you want to search for a date range up to the current date, do not edit the ‘Present’ date box.
Add the date from the builder to the query box.
Once you have finished adding terms to the query box, click Search (or Add to History) to run the search.

Enter dates using the format yyyy/mm/dd [date field]. The month and day are optional.

Use a Boolean operator when combining a date with other search terms.

Use the Boolean operator AND to limit your search to a specific publication date.

The available date fields are:

Date of Publication [dp] - Date searching includes both print and electronic dates of publication. Searching for a single date does not include items when the electronic date of publication is after the print date.
Electronic Date of Publication (if applicable) [epdat]
Print Date of Publication (if applicable) [ppdat]
Entry Date [edat] - Date used for PubMed processing, such as “Most Recent” sort order.
MeSH Date [mhda] - The date the citation was indexed with MeSH terms.
Create Date [crdt] - The date the PubMed record was first created.

Enter date ranges using a colon (:) between each date followed by a [date field].

Use a Boolean operator when combining a date range with other search terms.

Use the Boolean operator AND to limit your search to a date range.

Comprehensive searches for a full year should be entered as 2000:2000[dp] rather than 2000[dp] to retrieve citations with a different print and electronic year of publication.

Date range searching includes both print and electronic dates of publication.

Use the following format to search for a relative date range:

term="last X days" [date field]
term="last X months" [date field]
term="last X years" [date field]

where X is the number of days, months or years immediately preceding today’s date and [date field] is the date field tag: [dp], [edat] or [crdt].

The relative date range search for publication dates will also include citations with publication dates after today's date; therefore, citations with publication dates in the future will be included in the results.

You can use filters to narrow your search results by article type , text availability , publication date , species , article language , sex , age , and other .

To apply a filter:

Run a search in PubMed.
Click the filter you would like to activate from the sidebar. A check mark will appear next to the activated filter(s).
Subsequent searches will be filtered until the selected filters are turned off, or until your browser data is cleared.

The most popular filters are included on the sidebar by default. To display additional filters on the sidebar:

Click the "Additional filters" button.
A pop-up menu will appear showing the available filters for each category: article type, species, article language, sex, age, and other.
Choose a category from the list of options on the left side of the menu: Article Type, Species, etc.
Within each category, select the filters you would like to add to the sidebar.
Click Show. This will close the pop-up menu and display your selections on the sidebar with the other filters.
If you would like to cancel your selections, click Cancel or click on the X in the upper right corner to close the pop-up and return to your search results.
To apply the filter(s) to your search, click the filter(s) on the sidebar.

More information about filters:

When filters are selected a "Filters applied" message will display on the results page.
Click an applied filter to turn it off.
To turn off all applied filters, click the "Clear all" link or the "Reset all filters" button.
Citations may be excluded for some filter selections because they have not yet completed the MEDLINE indexing process.
You can activate additional filters with My NCBI filters .
See Filter search strategies for the equivalent PubMed query for each filter.

Article type

Select article types to narrow your results based on the type of material the article represents, such as: Clinical Trial or Review.

You can add more article types to the sidebar using the Additional Filters button. The complete list of publication types found in PubMed is available.

These filters may exclude some citations that have not yet completed the MEDLINE indexing process because they rely on the Publication Type [pt] data for the citation; publication type data may be supplied by the publisher or assigned during the MEDLINE indexing process. However, the Systematic Review article type filter uses a search strategy to capture non-MEDLINE citations and citations that have not yet completed MEDLINE indexing in addition to citations assigned the systematic review publication type.

Systematic reviews

To search for systematic reviews in PubMed, use the Systematic Review article type filter on the sidebar, or enter your search terms followed by AND systematic[sb] in the search box. For example, lyme disease AND systematic[sb].

The Systematic Review filter uses a search strategy in addition to the Systematic Review publication type [pt] to find systematic reviews in PubMed. To limit your search to only those citations with the Systematic Review publication type, use the publication type search tag[pt], i.e., systematic review[pt]; however, this may exclude some relevant citations that have not yet completed the MEDLINE indexing process.

Text availability

To filter your results to only citations that include a link to full text, a link to free full text, or an abstract, click the appropriate selections.

Alternatively, you may search for citations with links to full text, free full text or include an abstract using the values: full text[sb], free full text[sb], or 'hasabstract'. No search field tag is required for hasabstract. You may also search for all MEDLINE citations with a structured abstract with ‘hasstructuredabstract’.

Note: Most citations in PubMed to articles published before 1975 do not include abstracts.

Publication date

To filter your results by Publication Date, click 1 year, 5 years, 10 years, or enter a custom range. These filters include both electronic and print publication dates.

Species selections restrict your results to human or animal studies.

You can add species filters to the sidebar using the Additional Filters button.

These filters may exclude some citations because they have not yet completed the MEDLINE indexing process.

Article language

Language filters restrict your search to articles published in the selected language(s). You can add language filters to the sidebar using the Additional Filters button.

By default, PubMed displays English language titles and abstracts when provided by the publisher. Check the Abstract display for links to view the abstract in other languages (when available).

Sex restricts your search results to a specific sex for an animal or human study.

You can add sex filters to the sidebar using the Additional Filters button.

This filter may exclude some citations because they have not yet completed the MEDLINE indexing process.

Age filters restrict results to a specific age group for a human study.

You can add age filters to the sidebar using the Additional Filters button.

Age filters include:

Child: birth-18 years
Newborn: birth-1 month
Infant: birth-23 months
Infant: 1-23 months
Preschool Child: 2-5 years
Child: 6-12 years
Adolescent: 13-18 years
Adult: 19+ years
Young Adult: 19-24 years
Adult: 19-44 years
Middle Aged + Aged: 45+ years
Middle Aged: 45-64 years
Aged: 65+ years
80 and over: 80+ years

Other filters & more subsets

Exclude preprints.

The Exclude preprints filter can be added to the sidebar using the Additional Filters button. Alternatively, you can exclude preprints from your search results by including NOT preprint[pt] at the end of your query.

See Preprints for more information about preprint citations in PubMed.

MEDLINE Subset

The MEDLINE filter can be added to the sidebar using the Additional Filters button. To use this filter in a query, add medline[sb] to your search. The MEDLINE filter limits results to citations that are indexed for MEDLINE .

PubMed Central Subset

To restrict retrieval to citations that have a free full text article available in PubMed Central (PMC), search "pubmed pmc"[sb].

Use the PMID/PMCID/NIHMSID Converter to convert IDs for publications referenced in PubMed and PMC. To retrieve citations that include an NIHMS ID use the query, hasnihmsid.

Citation Status Subsets

The citation status indicates the internal processing stage of an article in the PubMed database (see PubMed Citation Status Subsets ).

To search for a particular citation status, enter one of the search terms below followed by the [sb] search tag:

pubmednotmedline

To search for the total number of PubMed citations, enter all[sb] in the search box.

Ahead of Print Citations

Publishers may submit citations for articles that appear on the web prior to their publication in final or print format. To search for these ahead-of-print citations, enter pubstatusaheadofprint.

Many phrases are recognized by the subject translation table used in PubMed's Automatic Term Mapping (ATM) . For example, if you enter fever of unknown origin, PubMed recognizes this phrase as a MeSH Term.

You can bypass ATM and search for a specific phrase using the following formats:

If you use quotes and the phrase is not found in the phrase index , the quotes are ignored and the terms are processed using automatic term mapping. The message "Quoted phrase not found in phrase index" will display at the top of your search results.
If you use a search tag and the phrase is not found in the phrase index , the phrase will be broken into separate terms, e.g., "psittacine flight" is not in the phrase index, so a search for psittacine flight[tw] is broken up and translated as: ((("psittaciformes"[MeSH Terms] OR "psittaciformes"[All Fields]) OR "psittacine"[All Fields]) OR "psittacines"[All Fields]) AND "flight"[Text Word]
If you use a hyphen and the phrase is not found in the phrase index , the search will not return any results for that phrase.

When you enter search terms as a phrase, PubMed will not perform automatic term mapping that includes the MeSH term and any specific terms indented under that term in the MeSH hierarchy. For example, "health planning" will include citations that are indexed to the MeSH term, Health Planning, but will not include the more specific terms, e.g., Health Care Rationing, Health Care Reform, Health Plan Implementation, that are included in the automatic MeSH mapping.

Phrase index

PubMed uses a phrase index to provide phrase searching. To browse the phrase index, use the Show Index feature included in the Advanced Search builder: select a search field, enter the beginning of a phrase, and then click Show Index.

Quoted phrase not found

Phrases may appear in a PubMed record but not be in the phrase index. To search for a phrase that is not found in the phrase index, use a proximity search with a distance of 0 (e.g., "cognitive impairment in multiple sclerosis"[tiab:~0] ); this will search for the quoted terms appearing next to each other, in any order.

Automated processes regularly add new phrases to the index based on standard criteria such as phrase frequency and length. If you would like to request a phrase be added to the phrase index, please write to the NLM Help Desk .

To search for all terms that begin with a word, enter the word followed by an asterisk (*): the wildcard character.

To search for a phrase including a truncated term, use the following formats:

Enclose the phrase in double quotes: "breast feed*"
Use a search tag: breast feed*[tiab]
Use a hyphen: breast-feed*

At least four characters must be provided in the truncated term.

The truncated term must be the last word in the phrase.

Truncation turns off automatic term mapping and the process that includes the MeSH term and any specific terms indented under that term in the MeSH hierarchy. For example, heart attack* will not map to the MeSH term Myocardial Infarction or include any of the more specific terms, e.g., Myocardial Stunning; Shock, Cardiogenic.

PubMed applies an AND operator between concepts, e.g., "vitamin c common cold" is translated as vitamin c AND common cold. Enter Boolean operators in uppercase characters to combine or exclude search terms:

AND retrieves results that include all the search terms.
OR retrieves results that include at least one of the search terms.
NOT excludes the retrieval of terms from your search.

PubMed processes searches in a left-to-right sequence. Use parentheses to "nest" concepts that should be processed as a unit and then incorporated into the overall search.

PubMed uses automatic term mapping to identify concepts. For example, for the search air bladder fistula, PubMed will search "air bladder" as a phrase. If you do not want this automatic phrase parsing, enter each term separated by the Boolean operator AND, e.g., air AND bladder AND fistula.
Search Details show how a search was translated.

You can search for a term in a specific field by including a search field tag after the term; for example, UCLA[ad] will search for the term “UCLA” in the affiliation field only.

More information about using search field tags:

The search field tag must be enclosed in square brackets.
Case and spacing do not matter: crabs [mh] = Crabs[mh].
Search field tags turn off Automatic Term Mapping (ATM) , limiting your search to the specified term only.
Using a search field tag after multiple terms will attempt to search those terms as a phrase : kidney allograft[tiab].
To search multiple terms in the same field, each term must be tagged individually: covid-19[ti] vaccine[ti] children[ti].
The Advanced Search builder can help you search for terms in specific fields and build large, complex search strings.

Search field tags

Affiliation [ad], all fields [all], article identifier [aid], author [au], author identifier [auid], book [book], comment correction type, completion date [dcom], conflict of interest statement [cois], corporate author [cn], create date [crdt], ec/rn number [rn], editor [ed], entry date [edat], filter [filter] [sb], first author name [1au], full author name [fau], full investigator name [fir], grants and funding [gr], investigator [ir], isbn [isbn], journal [ta], language [la], last author name [lastau], location id [lid], mesh date [mhda], mesh major topic [majr], mesh subheadings [sh], mesh terms [mh], modification date [lr], nlm unique id [jid], other term [ot], pagination [pg], personal name as subject [ps], pharmacological action [pa], place of publication [pl], pmcid and mid, pmid [pmid], publication date [dp], publication type [pt], publisher [pubn], secondary source id [si], subset [sb], supplementary concept [nm], text words [tw], title/abstract [tiab], transliterated title [tt], volume [vi].

You can use proximity searching to search for multiple terms appearing in any order within a specified distance of one another in the [Title], [Title/Abstract], or [Affiliation] fields.

To create a proximity search in PubMed, enter your terms using the following format:

"search terms"[field:~N]

There is no limit to the number of words you can search together in proximity; however, the more terms you enter, the more restrictive your search becomes. Using the Boolean operator AND to combine terms may be more appropriate than combining many terms into one proximity search.
Proximity searching is only available in the Title , Title/Abstract , and Affiliation search fields.
You can use the full search field tags [Title], [Title/Abstract], and [Affiliation], or the abbreviated versions [ti], [tiab], and [ad].
What N value to use will depend on your search. Try changing the N value and comparing the results to find what works best for your search.
A higher N creates a broader, more comprehensive search; this will typically retrieve more results overall, but some of these results may be less relevant. Using the Boolean operator AND to combine terms may be more appropriate than proximity searching with a large N value.
A lower N creates a narrower, more precise search; this will typically retrieve fewer results that are highly relevant, but may exclude other relevant results.
If N=0, the quoted terms will appear next to each other--with no other words in between.
For the affiliation field only, an N value of 1,000 or less will search for the double quoted terms together within the same affiliation, rather than spread across all affiliations on the record. See Affiliation [ad] for an example proximity search in the affiliation field and more information about searching for affiliations.

More information about proximity searching:

Results will include your quoted terms in any order. If you would like to search for an exact phrase with terms appearing in a specific order, use a phrase search instead.
Automatic Term Mapping is not applied to the quoted terms.
Proximity searching is not compatible with truncation (*). If the double quoted terms in a proximity search include a wildcard (*), the proximity operator will be ignored.
You can combine proximity searches with other terms using Boolean operators; for example, "hip pain"[Title:~4] AND stretching
Booleans and stopwords included in quoted terms for proximity search are searched like regular keywords.

Search PubMed for citations with the terms "rationing" and "healthcare" appearing within 2 words of each other--in any order--in the Title field:

Search results may include: rationing healthcare, healthcare rationing, rationing of healthcare, rationing in healthcare, rationing universal healthcare, rationing strategies in healthcare, rationing limited healthcare… and more.

Search PubMed for citations with the terms "patient," "physician," and "relationship" appearing next to each other—in any order—in the Title/Abstract fields:

Since N=0, the quoted terms must appear next to each other with no other words in between them, although they can still appear in any order.

You can build queries that combine proximity searches with other terms using Boolean operators (AND, OR, NOT):

Display, Sort, and Navigate

Understanding your search results, display an abstract, changing the display format of search results.

Showing more results

Sorting your results

Finding the full text article, similar articles, grants and funding, navigating searches with more than 10,000 results, discovering related data in ncbi databases, find related resources using linkout, reporting broken or problem links.

Citations are initially displayed 10 items per page and sorted by Best Match.

By default, PubMed search results are displayed in a summary format and include snippets from the citation abstract. Snippets and highlighted terms are selected based on relatedness to your query.

To see the abstract for an individual citation, click the title of the citation to go to its abstract page.

Journal names are shown using the journal title abbreviation. When viewing citations in Abstract format, you can mouseover a journal’s title abbreviation to display the full journal name.

Click the title of the citation to go to its abstract page, or change the search results display to Abstract format using the Display options button in the upper right corner of the search results page.

PubMed may include non-English abstracts if supplied by the publisher. The abstract text defaults to English when a citation has an accompanying non-English abstract. Links to display the additional language(s) are available on the Abstract display. To retrieve citations with non-English abstracts, use the query hasnonenglishabstract.

Results are displayed in the summary format by default, except a single citation result will go directly to the abstract page. You can change the results format using the Display options button:

Click the Display options button in the upper right corner of the search results page
Select the display format you would like to use
Results will be displayed in the new format

Selecting one or more items and changing the display format will display only the selected result(s) in the new format.

By default, the summary format includes snippets from the citation abstract. You can turn off snippets under Display options by deselecting Abstract snippets.

The results page indicates the total number of items retrieved.

Ten items are displayed per page by default. You can change the number of items displayed per page using the Display options button:

Select the number of items to display per page: 10, 20, 50, 100, or 200
Your selection will be active for subsequent searches until your browser cookies are cleared.

Click "Show more" to display the next page of results, or click "Jump to page" to navigate directly to a specific page of results.

The default sort order in PubMed is Best Match. You can use the "Sort by" drop-down menu at the top of the search results page to change the sort order.

If you change the sort order, your new selection will be active for subsequent searches until your browser cookies are cleared.

Sort orders

You can sort your search results by:

Best Match: The Best Match sort order is based on an algorithm that analyzes each PubMed citation found with your search terms. For each search query, "weight" is calculated for citations depending on how many search terms are found and in which fields they are found. In addition, recently-published articles are given a somewhat higher weight for sorting. The top articles returned by the weighted term frequency algorithm above are then re-ranked for better relevance by a new machine-learning algorithm. Please see the Algorithm for finding best matching citations in PubMed for more information.
Most Recent: Citations sorted by Most Recent are displayed in reverse date added order: last in, first out. The Most Recent date is the date a record was initially added to PubMed, not the publication date. The secondary sort is PMID.
Publication Date: Citations sorted by Publication Date are displayed in reverse chronological order: newest to oldest. Citations with more than one publication date, such as electronic and print, are sorted by their earliest publication date. Publication dates without a month are set to January, multiple months (e.g., Oct-Dec) are set to the first month, and dates without a day are set to the first day of the month. Dates with a season are set as: winter = January, spring = April, summer = July and fall = October.
First Author: Citations are sorted alphabetically by first author name. The secondary sort order within a group with the same first author is PMID.
Journal: Citations are sorted alphabetically by journal name. The secondary sort order within a group with the same journal name is PMID.

Reverse sort order

When sorting by Most Recent, Publication Date, First Author, or Journal, you can reverse the sort order by clicking the up/down arrow next to the selected sort option to toggle between ascending or descending order.
The reverse sort option will not display when Best Match sort order is selected.

Computed author sort

Clicking an author name link on the abstract display runs a search for the author in PubMed. If an author name is computationally similar with an author name for additional PubMed citations, the results will display those citations first, in ranked order, followed by the non-similar citations. Author name disambiguation details are available in Liu W and Wilbur WJ .

PubMed records contain citation information (e.g., title, authors, journal, publication date) and abstracts of published articles and books. PubMed search results do not include the full text of the journal article, but the abstract view in PubMed includes links to the full text from other sources when available, such as the publisher’s website or the PubMed Central (PMC) database. The full text journal site may require a fee or subscription, however online journals sometimes provide free access. Access may also be available through your organization, or local medical library.

You may be able to obtain free copies of full text articles in these ways:

Free full text filter

On the filter sidebar, click "Free full text" to narrow results to resources that are available for free on the web, including PubMed Central, Bookshelf, and publishers' websites. Alternately, include free full text[Filter] in your query.

PubMed Central

When full text is available in PubMed Central (PMC) , the "Free in PMC" icon will appear on the citation's abstract display under Full Text Links. Click the icon to view the article in PMC.

PubMed Central (PMC) is the U.S. National Institutes of Health (NIH) free digital archive of biomedical and life sciences journal literature.

From the publisher

Journal publishers or related organizations may provide access to articles for free, for free after registering as an individual or guest, or for a fee. When provided by the publisher or other organization, icons linking to these sources can be found on the citation's abstract display under the "Full Text Links" and/or "LinkOut" sections. Icons will often indicate free full text when the article is available for free.

Note: When you click a full text icon or link in PubMed, you leave PubMed and are directed to the full text at an external provider's site. NCBI does not hold the copyright to this material, and cannot give permission for its use. Users should review all copyright restrictions set forth by the full text provider before reproducing, redistributing, or making commercial use of material accessed through LinkOut.

Please see the Copyright and Disclaimers page for additional information.

If you are affiliated with a hospital, university, or other institution

Your local medical library is your best option. If you see icons for your library on the abstract view this indicates that your library provides a link to the article, has the journal in its collection, or may otherwise obtain the article for you through interlibrary loan. If your library does not have access to the article you need, ask a librarian about ordering the article from another institution.

Local library

Some local libraries have copies of medical journals or can get a copy of an article for you. Ask your local librarian about inter-library loan options and fees.

PubMed abstracts include figures when the full text article is available in PubMed Central (PMC) . Click the thumbnail to view a larger version of the image, caption, and link to the figure and copyright information in PMC.

The abstract page for a citation includes links to PubMed citations for similar articles. The "See all similar articles" link will retrieve a pre-calculated set of PubMed citations that are closely related to the selected article:

Similar articles are displayed in ranked order from most to least relevant, with the "linked from" citation displayed first.
Similar articles are generated by comparing words from the title, abstract, and MeSH terms using a word-weighted algorithm.
Filters are not activated for similar articles.
You can refine the list of similar articles using your search History , where the similar articles retrieval is represented as a list of PMIDs. Use this search number in a search. Refining the list removes the ranked order and may remove citations that are most relevant.

See Computation of similar articles for more information.

PubMed abstracts include links to other resources citing the current item. "Cited by" is generated using data submitted by publishers and from NCBI resources, when available. "Cited by" may not be a complete list of works citing a particular item.

PubMed abstracts include references when available. Reference lists are available for citations to full text articles included in the open access subset of PMC and for citations where the publisher supplied references in the citation data sent to PubMed.

PubMed displays grant numbers, contract numbers, and intramural research identifiers that have been associated with a publication by:

Publishers when depositing data in PubMed and PubMed Central;
depositing a manuscript through the NIH Manuscript Submission (NIHMS) or Europe PMC Plus system; or
when adding a publication to My Bibliography ; and/or
NLM text mining and indexing processes.

A grant award or contract may be acknowledged in an article and, therefore, displayed in PubMed, for various reasons, including support for activities that contributed directly to the publication as well as support for the generation of an underlying dataset or another shared resource. Additionally, some articles may not explicitly acknowledge intramural research support, yet the authors may be affiliated with a funding agency and may have associated their intramural support with a PubMed record at the time of manuscript deposit to PMC.

Funding information in PubMed is collected in or converted to a standardized format when possible to enable broad discovery and impact monitoring. For example, if a publication acknowledges support from NIH grant number 1R01 GM987654-01-A1 or GM987654 or ROI GM987654 in a publication, in PubMed the funding information would be normalized to R01 GM987654, consistent with NIH requirements for proper grant number format. Funding associations made in a manuscript submission, grant reporting, or indexing system use standardized project identifiers provided to NLM by the organization administering the funding. To learn about searching funding information, see the search field section on Grants and funding [gr] .

The scope of funding information included in PubMed has expanded over time to support the public access policies of NIH and other funding organizations . Since 1981, NLM has included grant or contract numbers or both that designate financial support by any agency of the United States Public Health Service (PHS), including NIH. Until 2000, only up to three grant numbers were included. Beginning in March 2006, funding information was expanded in PubMed to include grant, contract, and intramural funding assertions made in NIHMS and My Bibliography to support the NIH Public Access Policy. Publishers have been able to supply funding information directly to PubMed since January 2017. For more information on the history of funding information in PubMed, see the Grant Number section of MEDLINE/PubMed Data Element (Field) Descriptions .

Reporting funding information errors

Some publications may be inadvertently linked to the wrong funding information. For example, the association of a publication to NIH-funded extramural research requires that the author(s) acknowledge NIH support in the article and that the acknowledgement be in a form that can be readily associated with a specific grant or contract. Variations in the format used to cite NIH funding may lead to either an inability to make an association or erroneous matches of publications to grants and contracts.

If you identify an error in funding information associated with a PubMed record, please contact the NLM help desk . NLM will not remove funding associations that reflect the acknowledged funding in the article without a published correction to ensure alignment with the scientific record. If an award association was provided by the author, principal investigator, or project director in My Bibliography or the NIHMS for formal NIH progress and public access compliance reporting, removing the association requires the principal investigator be notified and confirm the lack of direct support.

PubMed can display up to 10,000 results. The following options can help you navigate searches with more than 10,000 results:

Reverse the sort order to see the last results first.
Divide the result set into smaller chunks using the results timeline or custom date range filter .
Adjust your search to retrieve fewer results.
For programmatic use and bulk downloads, PubMed data is available via FTP .

When available, links to other related NCBI databases are included on a citation's Abstract page under the Related information section. The complete list of database options is provided in Entrez Link Descriptions .

MEDLINE indexed citations include additional supplemental information on the Abstract page such as MeSH terms, publication types, and substances with links to search for these data in PubMed and the MeSH Database.

To simultaneously search all NCBI databases, use the NCBI Search page .

Most PubMed records include LinkOut resources to a variety of websites including publishers, aggregators, libraries, biological databases, and sequence centers. LinkOut resources link to providers’ sites to obtain the full text of articles or related information, e.g., consumer health. There may be a charge to access the text or information from a provider's site.

To view LinkOut resources, navigate to the LinkOut section at the end of an individual citation's abstract page.

To find citations with links to free full text articles, apply the "Free full text" filter to your search results.

To find citations with links to full text articles, enter search terms followed by AND full text[sb].

Cite, Save, and Share

Save citations temporarily using the clipboard, save citations indefinitely using my ncbi collections, save citations as a text file, cite an article, export citations into citation management software, email citations, create an email alert for a search, create an rss feed for a search, print your search results, get a permalink to bookmark or share your search, download pubmed data.

The Clipboard provides a place to collect up to 500 items from one or more searches. Items saved to the Clipboard are stored in your browser cookies and will expire after 8 hours of inactivity. If you would like to save items for longer than 8 hours or to view on another device, please use Send to: Collections .

To add items to the Clipboard:

Use the check boxes to select items from your search results. To save all results (up to a maximum of 500), do not tick any check boxes.
Use the Send to button and choose Clipboard.
If no items were selected, a drop-down menu of options will display where you may add selected items, all results on the page, or all results (up to a maximum limit of 500 citations) to the Clipboard.
An individual item can also be added to the Clipboard from its abstract page.
To view your selections, click the Clipboard link under the Search bar. This link will only appear after one or more items have been added to the Clipboard; the link is not present when the Clipboard is empty.

To delete items from the Clipboard:

On the Clipboard page, click "Remove from Clipboard" below each item to delete the item from the Clipboard.
Select one or more items using the check boxes next to each item, then click "Remove selected items."
To delete all items from the Clipboard, click "Remove all."

More information about the Clipboard:

Citations added to the Clipboard are marked with the message "Item in Clipboard" in search results.
The maximum number of items that can be sent to the Clipboard is 500. If you select Clipboard from send to without selecting citations, PubMed will add all (up to 500 citations) of your search results to the Clipboard.
The Clipboard will not add a citation that is currently in the Clipboard; it will not create duplicate entries.
Your web browser must accept cookies to use the Clipboard.
Citations in the Clipboard are represented by the search number #0, which may be used in Boolean search statements. For example, to limit the citations you have collected in the Clipboard to English language articles, use the following search: #0 AND english [la]. This does not affect or replace the Clipboard contents.

Search results can be saved in My NCBI using the Collections feature. There is no limit to the number of collections you may store in My NCBI. In addition, collections can be made public to share with others.

To save results to a new collection:

Sign into My NCBI. Run a search in PubMed.
Use the check boxes to select items from your search results or Clipboard. To save all results (up to a maximum of 1,000), do not tick any check boxes.
Use the Send to button and choose Collections.
If no items were selected, a drop-down menu of options will display where you may add selected items, all results on the page, or all results (up to a maximum limit of 1,000 citations) to a Collection.
An individual item can also be added to a Collection from its abstract page.
Choose Create a new collection.
Name your collection using a short, meaningful title. The name must be unique and less than 100 characters. Identical names for different Collections are not allowed.
Click Add to finish.

As you continue to build collections, you may want to add new items to an existing collection. To add search results to an existing collection:

Follow steps 1 - 4 above. Add to an existing collection will be the default selection.
Use the pull-down menu to choose a collection.

For more information on viewing, sorting, editing, merging, sharing, and deleting collections, see Collections in My NCBI Help.

Use the Save button to download citations to a text file.

Use the check boxes to select citations from your search results or Clipboard. You may move to other pages to continue your selections. If you do not make any selections, you can choose to save “All results on this page” or “All results” from the Save menu.
Selection: The number of selected items will be shown, for example: Selection (87).
All results on this page
All results (up to a maximum of 10,000 citations)
Format: Summary (text), PubMed , PMID list, Abstract (text), or CSV
Click Create file.
Your web browser will prompt you to save the file on your computer.

More information about saving citations to a file:

Saving a large set of results may take several minutes.
To save citations in HTML format, use the "Save" or "Save as" function of your browser and change the file extension to html. When saving as HTML, only those citations displayed on the page will be saved; therefore, consider showing more results .

The Cite button makes it easy to retrieve styled citations that you can copy and paste into a document, or download an .nbib file to use with your reference manager software.

Using the Cite button for an item will open a pop-up window where you can copy the citation formatted in four popular styles: AMA (American Medical Association), MLA (Modern Language Association), APA (American Psychological Association), or NLM (National Library of Medicine). You can also download the citation as an .nbib file, which most bibliographic reference management software can import.

Note: In all citation styles, there are certain capitalization rules that machines cannot handle. For example, there is no way to identify proper nouns, acronyms, abbreviations, etc., that is 100% accurate and complies with all rules at all times. Capitalization of article titles and other citation elements should be checked for compliance with a particular reference style when required.

To export multiple citations: follow the instructions for saving citations as a text file and choose the format Summary (text) to save a list of citations in NLM style, or follow the instructions to export citations into your citation management software program .

Use Send to: Citation Manager to export citations as an .nbib file that can be used by many citation management programs:

Use the check boxes to select citations from your search results or Clipboard. You may move to other pages to continue your selections. Alternately, you can choose to save all results on this page or all results from the Send to: Citation Manager menu.
Click Send to and choose Citation Manager.
Confirm the citations you want to export: selection, all results on this page, or all results (up to a maximum of 10,000).
Import this saved file into your citation management program.

You can also download an .nbib file for individual citations using the Cite button.

Questions regarding citation management software should be directed to the respective companies.

Use the check boxes to select citations from your search results or Clipboard. You may move to other pages and continue your selections. You may also choose to email all citations shown on the page without making any selections.
Click the Email button.
Enter an email address. Select which citations to send and the format.
Click Send email. The system returns you to your results page and displays a confirmation e-mail sent message.

More information about emailing citations:

Your citations will be sent from the NCBI automatic mail server with the sender's email address [[email protected]]. Do not reply to this message, as this is not a functioning customer service email address and is not monitored.
The CAPTCHA image does not display for users who are signed in to My NCBI.

Click "Create alert" under the search bar to create an automatic email update for searches. You must sign in to My NCBI to use this feature. See Saving and Managing Searches for more information.

Click on Create RSS under the search box at the top of the page to create an RSS feed for your search.

The RSS feed name will default to the search terms. You can edit the RSS feed name as needed.
Use the pull-down menu to select the number of items displayed. You may manually edit the limit= parameter in the RSS feed link created in Step 4 to display up to a maximum of 1000 items. Please note that increasing this limit will also increase the loading time.
Click the Create RSS button.
The RSS Feed Link will appear; click on Copy to copy the link.
Use this link with your feed reader or other application.

Use the print function of your web browser. To print citations from different searches, save the citations in PubMed’s Clipboard , and then print.

Changing the display format

To get the URL for an individual citation, copy the permalink for the citation under "Share."

To get the URL for your search results, copy the URL from your web browser's address bar or bookmark the URL using your web browser's bookmark function.

To create a URL manually:

Use the base URL: https://pubmed.ncbi.nlm.nih.gov/?term=search
Replace “search” in the base URL with your query terms
Escape spaces by converting them to plus signs (+); for example, Biochem Soc Trans should be entered as: Biochem+Soc+Trans

The number of characters you can use may be limited by your browser’s maximum URL length (which may be different for each browser).

Search PubMed for articles about antioxidant and chocolate:

Optional search parameters:

format=summary, abstract, pubmed, pmid
sort=relevance, date, pubdate, fauth, jour
sort_order=asc
size=10, 20, 50, 100, 200

Search PubMed for articles about breast cancer, sorted by ascending publication date (oldest to newest), and display 50 citations per page:

Advanced Search

Searching in a specific field, browsing the index of terms, previewing the number of search results, combining searches using history, viewing the search details.

Tools included on the Advanced Search page help users to: search for terms in a specific field, combine searches and build large, complex search strings, see how each query was translated by PubMed, and compare number of results for different queries.

Use the Advanced Search Builder to search for terms in a specific field, such as author or journal. For some fields, an autocomplete feature will provide suggestions as you type.

From the "All Fields" drop-down menu, select the field you would like to search.
Add terms from the builder to the query box to construct your search. The default Boolean operator is AND; if desired, choose OR or NOT from the pull-down menu.

You may also search a specific field -- and bypass Automatic Term Mapping -- by adding a search field tag to a term.

The Advanced Search Builder includes the Show Index feature, which provides an alphabetical display of terms appearing in selected PubMed search fields. You can browse by all fields or within specific fields such as MeSH Terms.

Click Advanced to navigate to the Advanced Search page, and use the Builder to select a search field from the All Fields menu. Note: Show Index is not available for every search field. The Show Index link will only display for fields that are compatible with this feature.
Enter a term in the search box, then click Show Index.
The index displays an alphabetic list of search terms and the approximate number of citations for each term (the actual citation count is returned when the search is executed).
Scroll until you find a term you want to include in your search, and then highlight it to add it to the search box.
Multiple terms may be selected from the list and added to the search box.
Add terms from the builder to the query box to construct your search.

More information about using the index:

PubMed processes all Boolean operators left to right.
The builder will automatically OR (and add parentheses) for multiple terms selected from the index.
A slash will display after a space. For example, the MeSH Term and Subheading "zika virus/analysis" will display after "zika virus infection/virology." Enter MeSH terms followed by a slash to go directly to the display for the MeSH/Subheading combination counts in the index.
Show Index is not available for date fields.

Your PubMed search history appears on the Advanced Search page under History. This feature requires your web browser to accept cookies.

Descriptions of each column in the History table appear below:

Search numbers may be used in place of the search string itself when combining queries (e.g., #1 OR #2).
A repeated query will move to the top of History but will retain its original numbering.
History is limited to the last 100 searches. Once the maximum number is reached, PubMed will remove the oldest search from history and add the most current search.
Actions: Add, delete, or save a query. Adding queries from History places the search string into the Query box to be used in the next search. Deleting a query removes it from History.
Query: This column shows previous search strings as entered by the user.
Details: PubMed may modify or add search terms to a search to optimize retrieval, e.g., using automatic term mapping. Click the chevron icon " > " to expand search details and see how the search was translated.
Results: The total number of citations retrieved for that query. Click the number to run the search and see the results in PubMed.
Time: Timestamp of when the search was conducted.
Please note, Microsoft Excel is typically unable to display or print more than a maximum of 1024 characters in a cell; therefore, you may want to open the CSV file with a text editor to display your complete searches.
Delete: Click "Delete" to remove all queries from History; otherwise, History expires after 8 hours of inactivity.
Click Advanced to navigate to the Advanced Search page.
Use the builder to add search terms to the query box, or type your search directly into the query box.
Use the split button to toggle the button function from "Search" to "Add to History".
Click Add to History. This will run the search without leaving the Advanced Search page.
See your query including the number of results in the History table.

Searches can be combined or used in later searches using your search History.

In the History table, click the More Actions icon " ... " next to your query.
From the available options, select "Add query" to copy the query to the Query box.
After you've added content to the Query box, options to use the Boolean operators AND, OR, or NOT will appear when adding more queries to the Query box.
Edit your query in the Query box if you would like to make any changes before running the search.
Click Search (or Add to History).

More information about combining searches from your History:

Citations in the Clipboard are represented by the search number #0, which may be used in searches. For example, to limit the citations you have collected in the clipboard to English language citations, use the following search: #0 AND english [la]. This does not change or replace the Clipboard contents.

PubMed may modify or add additional search terms to your search to optimize retrieval, such as: MeSH terms, British/American spellings, singular/plural word forms, and other synonyms.

Search Details are included on the Advanced Search page under History.
Click the chevron icon " > " next to a query in History to expand the Search Details.
When expanded, the details below a query in the History table show the search strategy used to run the search.

More information about search details:

Translations show individual term mappings using PubMed's search rules and syntax. Query terms without translations will not be listed in this section; for example, exact phrases bypass Automatic Term Mapping (ATM) .
Warnings are displayed for the original query with potential errors in bold and red type, such as syntax errors, terms not found, or invalid tags. Warnings also appear as a highlighted message in PubMed when the search is run or added to History.

Other services

Clinical queries, single citation matcher, search pubmed using the mesh database, search for journal information in the nlm catalog, using the e-utilities api tools, citation matcher api, batch citation matcher, consumer health.

PubMed Clinical Queries provides specialized searches for:

COVID-19 Articles

Clinical Study Categories

Medical Genetics

Search for COVID-19 articles

The COVID-19 article filters limit retrieval to citations about the 2019 novel coronavirus. Results are displayed in a column filtered by research topic categories. See COVID-19 article filters for the filter search strategies; these may evolve over time.

To find citations using the COVID-19 article filters:

Click Clinical Queries from the PubMed homepage
Enter your search terms in the search box
Click Search
Select a Category: General, Mechanism, Transmission, Diagnosis, Treatment, Prevention, Case Report, Forecasting, or Long COVID
Preview results in the COVID-19 Articles column
To view the results in PubMed, click the "See all" link below the results preview

To use the COVID-19 article filters in a query, add the filter name to your search with the search field tag [Filter], e.g., LitCPrevention[Filter]. The available filters are:

LitCGeneral
LitCMechanism
LitCTransmission
LitCDiagnosis
LitCTreatment
LitCPrevention
LitCCaseReport
LitCForecasting
LitCLongCOVID

Search PubMed for Remdesivir with the COVID-19 General filter:

Search by clinical study category

Clinical Study Categories use a specialized search method with built-in search filters that limit retrieval to citations reporting research conducted with specific methodologies, including those that report applied clinical research. See Clinical Study Categories filters for the filter search strategies.

To find citations using the Clinical Study Categories:

Select a Category: Therapy, Diagnosis, Etiology, Prognosis, or Clinical Prediction Guides
Select a Scope: Narrow (specific search) or Broad (sensitive search)
Preview results in the Clinical Study Categories column

Medical genetics searches

The Medical Genetics filters limit retrieval to citations related to various topics in medical genetics. See Medical genetics search filters for the filter search strategies.

To use a Medical Genetics filter, add the filter name to your search with the search field tag [Filter], e.g., Genetic Testing[Filter]. The available filters are:

Differential Diagnosis
Clinical Description
Genetic Counseling
Molecular Genetics
Genetic Testing

Search PubMed for sickle cell anemia using the Genetic Counseling filter:

The Single Citation Matcher has a fill-in-the-blank form for searching for a citation when you have some bibliographic information, such as journal name, volume, or page number.

Click Single Citation Matcher on the PubMed homepage.
Enter the citation information.

More information about using the Single Citation Matcher:

The journal box includes an autocomplete feature that suggests titles as you enter a title abbreviation or full title. Titles displayed by the autocomplete menu are in ranked order based on the number of citations in PubMed.
After selecting a journal with special characters (e.g., ampersand, colon) when using the Back button to return to the Single Citation Matcher you must clear and reenter the title.
The author box also includes an autocomplete feature that suggests author names in ranked order based on the number of citations. Full author names may be searched for citations published from 2002 forward if the full author name is available in the article.
Click either the 'Only as first author' or ‘Only as last author’ check box to limit an author name to the first or last author.

MeSH (Medical Subject Headings) is the NLM controlled vocabulary thesaurus used for indexing PubMed citations.

Use the MeSH database to find MeSH terms, including Subheadings, Publication Types, Supplementary Concepts and Pharmacological Actions, and then build a PubMed search. The MeSH database can be searched by MeSH term, MeSH Entry Term, Subheading, Publication Type, Supplementary Concept, or MeSH Scope Note.

More information about the MeSH database:

An autocomplete feature is available from the search box.
Search results are displayed in relevance-ranked order, therefore, when a user’s search exactly matches a MeSH Term, that Term is displayed first.
Click the MeSH term from the Summary display or choose Full from the display format menu to view additional information and search specifications, such as Subheadings, restrict to Major MeSH Topic, or exclude terms below the term in the MeSH hierarchy.
Year Introduced is the year the term was added to MeSH. If more than one year is shown, the term was available for indexing back to the earliest year noted. Articles are indexed using the vocabulary in place at the time of indexing, therefore, the year introduced for a term and the date of publication of a citation indexed with that term may not agree.

Launch PubMed searches from the MeSH database

To build a PubMed search from MeSH:

Run a search in the MeSH database .
Select terms using the check boxes.
Click "Add to search builder" in the PubMed search builder portlet.
You may continue searching and including additional terms to the PubMed search builder using the "Add to search builder" and Boolean pull-down menu.
When you are finished, click "Search PubMed."

The NLM Catalog includes information about the journals in PubMed and the other NCBI databases.

Click Journals in NCBI Databases on the homepage of NLM Catalog or the Journals link on the PubMed homepage to limit your NLM Catalog results to the subset of journals that are referenced in NCBI database records.

See the NLM Catalog help for additional information.

Other journal resources include:

PubMed journals with links to full text
List of all journals included in PubMed via FTP
List of Serials Indexed for Online Users

E-utilities are tools that provide access to data outside of the regular NCBI web search interface. This may be helpful for retrieving search results for use in another environment. If you are interested in large-scale data mining on PubMed data, you may download the data for free from our FTP server . Please see the terms and conditions for data users.

Fielded search

Heuristic search, auto search, rate control.

The PubMed Citation Matcher API finds PubMed identifiers (PMIDs) for citation data in structured or raw form. The interface supports three retrieval methods:

field - runs a fielded search using core bibliographic information, such as journal, date, or volume.
heuristic - collects all input elements into a single string and returns the closest matching documents.
auto - combines the two above methods and switches to heuristic mode if the fielded search has not yielded a result. This is the default method.

More information about the Citation Matcher API:

The API supports both GET and POST requests.
Data is exchanged in JSON.
Input data should be UTF-8 encoded.
The API returns a maximum of 20 PMIDs; queries returning more than 20 PMIDs are treated as bad requests.

The API root is:

method=field runs a fielded search using core bibliographic information, such as journal, date, or volume. This functionality is similar to E-utilities ESearch ; users should select the API that best suits their needs.

For a structured search, the following fields can be used:

journal - the name of the journal
pdat - the publication date, in the format YYYY/MM/DD
volume - the volume of the publication
issue - the volume of the publication
authors - one or more author names, in the format "Surname Initial" (Doe J). Optionally, the position may be specified as first, last, or auto.

Example fielded search:

GET request URL:

POST request data:

method=heuristic collects all input elements into a single string and returns the closest matching documents. It is sufficient to supply a raw citation string, such as: "The role of drag in insect hovering. J. Exp. Biol. 2004;207:4147–4155."

Example heuristic search:

method=auto first runs a fielded search , and if no results are found, it combines the fields and runs a heuristic search . This is the default method.

Example auto search:

First, a fielded search is run but no results are found due to the specified author not appearing on the citation:

Then it concatenates the fields and runs a heuristic search, which returns the closest matching document(s):

When using the PubMed Citation Matcher API programmatically, we request that you limit your application's rate to 3 requests / sec and do not make concurrent requests to this service, even at off-peak times. Additionally, requests must contain the name of the calling project in the User-Agent HTTP header value; e.g. Hydra/1.3.15 .

Use the Batch Citation Matcher to retrieve PMIDs for multiple citations. The Batch Citation Matcher requires that you enter the bibliographic information (journal, volume, page, etc.) in a specific format.

To retrieve PubMed PMIDs:

Create citation strings for the items you would like to retrieve using the following format: journal_title|year|volume|first_page|author_name|your_key| Fields must be separated by a vertical bar with a final bar at the end of the string.
Enter your email address. Email messages may take several minutes to process and be sent to your email address.
Upload your citation strings as a text file (.txt) or enter each citation string on a separate line in the text box. If citation strings are entered in the text box and a file is uploaded, the results will be an aggregate of both.
Click search.

If a match is not found the citation string will display one of the following:

your_key|NOT_FOUND;INVALID_JOURNAL - The journal name is not valid. See the journal lists or the NLM Catalog to find the correct journal abbreviation.
NOT_FOUND - The journal name is valid, but the citation string did not find a match.
AMBIGUOUS - The information provided matches more than one citation. Citation information with 3 or fewer matches include the PMIDs, and more than 3 matches include the total PMID match count. Use the Single Citation Matcher or ESearch to retrieve all citations for searched fields.
Text (.txt) format must be used when uploading a file.
You may receive multiple emails for searches containing more than 2,000 citation strings.
Enter author names without punctuation as smith jc. Initials are optional.
Your key is any string you choose to tag the citation, it is returned unaltered.
The journal title field may include the full journal title or the NLM title abbreviation.
Each citation field is searched starting with the journal title until a unique match is found.
The journal title is a required field however you may omit other fields. If you omit fields you must retain the vertical bars in the citation string. For example, if you omit the volume number 88 from the first example below it should be entered as: proc natl acad sci u s a|1991||3248|mann bj|P32022-1|

Example input:

proc natl acad sci u s a|1991|88|3248|mann bj|P32022-1|
proc natl acad sci u s a|1992|89|3271|gould se|P26261-1|
proc natl acad sci u s a|1970|89|3271|smith|P26261-1|
res microbiol|1992|143|467|ivey dm|P25966-1|
science|1987|235|182|palmenberg ac|P12296-2|
eschatology|1993|12|22|public jq|C12233-2|
virology|1993|193|492|hardy me|Q02945-1|
virus genes|1992|6|393||P27423-1|
yeast|1992|8|253|sasnauskas k|P24813-1|

Example output:

proc natl acad sci u s a|1991|88|3248|mann bj|P32022-1|2014248
proc natl acad sci u s a|1992|89|3271|gould se|P26261-1|1565618
proc natl acad sci u s a|1970|89|3271|smith|P26261-1|NOT_FOUND
res microbiol|1992|143|467|ivey dm|P25966-1|1448623
science|1987|235|182|palmenberg ac|P12296-2|3026048
C12233-2|NOT_FOUND;INVALID_JOURNAL
virology|1993|193|492|hardy me|Q02945-1|8382410
virus genes|1992|6|393||P27423-1|1335631
yeast|1992|8|253|sasnauskas k|P24813-1|1514324

The National Library of Medicine cannot provide specific medical advice. NLM urges you to consult a qualified health care professional for answers to your medical questions. NLM does not have pamphlets or other materials to mail.

MedlinePlus and MedlinePlus en español are specifically designed for consumers, containing hundreds of topic pages including NIH-written descriptive information, videos, health check tools, drug, herb and supplement info, links to Fact Sheets from other NIH Institutes, the CDC, etc., and more.

Further assistance and training

How pubmed works: automatic term mapping (atm), algorithm for finding best matching citations in pubmed, pubmed coverage, pubmed format, pubmed data field descriptions, nlm author indexing policy, error messages, mesh subheadings, pubmed character conversions, publication types, status subsets, filter search strategies, clinical queries filters, computation of similar articles, journal lists, contact customer support.

E-mail the PubMed Help Desk
Call the NLM Customer service desk: 1-888-FIND-NLM (1-888-346-3656)

Other NLM publications

PubMed Online Training
PubMed Trainer's Toolkit
NLM Technical Bulletin

Untagged terms that are entered in the search box are matched (in this order) against a Subject translation table (including MeSH (Medical Subject Headings) ), a Journals translation table, the Author index, and an Investigator (Collaborator) index.

When a match is found for a term or phrase in a translation table the mapping process is complete and does not continue on to the next translation table.

To see how your terms were translated, check the Search Details available on the Advanced Search page for each query under History. If you want to report a translation that does not seem accurate for your search topic, please e-mail the information to the NLM Help Desk .

1. Subject translation table

The Subject Translation Table contains:

British and American spellings
Pairs: singular and plural word forms, synonyms, and other closely related terms
Drug brand name to generic name translations
The See-Reference mappings (also known as entry terms) for MeSH terms
Pharmacologic action terms
Terms derived from the Unified Medical Language System (UMLS) that have equivalent synonyms or lexical variants in English
Supplementary concept (substance) names and their synonyms.

If a match is found in this translation table, the term will be searched as MeSH (that includes the MeSH term and any specific terms indented under that term in the MeSH hierarchy), and in all fields.

For example, if you enter child rearing in the search box, PubMed will translate this search to: "child rearing"[MeSH Terms] OR ("child"[All Fields] AND "rearing"[All Fields]) OR "child rearing"[All Fields]

If you enter a MeSH Term that is also a Pharmacologic Action PubMed will search the term as [MeSH Terms], [Pharmacologic Action], and [All Fields].

If you enter an entry term for a MeSH term the translation will also include an all fields search for the MeSH term associated with the entry term. For example, a search for odontalgia will translate to: "toothache"[MeSH Terms] OR "toothache"[All Fields] OR "odontalgia"[All Fields] OR "odontalgias"[All Fields] because Odontalgia is an entry term for the MeSH term toothache.

Substance name mappings do not include a mapping for individual terms in a phrase, e.g., IL-22 will not include IL[All Fields] AND 22[All Fields].

MeSH term mappings that include a standalone number or single character do not include a mapping for individual terms in a phrase, e.g., Protein C will not include Protein[All Fields] or C[All Fields].

2. Journals translation table

The Journals translation table contains the:

full journal title
title abbreviation
ISSN and eISSN number.

These will automatically map to the journal abbreviation that is used to search journals in PubMed and in all fields. For example, a search for endocrine pathology will translate to: "Endocr Pathol"[Journal] OR ("endocrine"[All Fields] AND "pathology"[All Fields]) OR "endocrine pathology"[All Fields]

3. Author index

If the term is not found in the above tables, and is not a single term, PubMed checks the author index for a match. The author index includes author names and initials, as well as full author names for articles published from 2002 forward, if available.

PubMed automatically truncates a search for an author's name to account for varying initials, e.g., o'brien j retrieves o'brien ja, o'brien jb, o'brien jc jr, as well as o'brien j.
When combining multiple authors, to avoid a match with full author names, include initials or use the [au] search tag, e.g., ryan[au] james[au]. Author names comprised of only stopwords, e.g., as a, are not searched as authors if they are part of phrase, chemical burn as a danger, unless the search only includes the author name, e.g., as a.
Initials and suffixes are not required, if you include a middle initial or suffix, you will only retrieve citations for articles that were published using the middle initial or suffix.
To distinguish author initials that may match a full author name use the [fau] search tag, e.g., peterson do[fau].

4. Investigator (Collaborator) index

If the term is not found in the above tables, except for Author, and is not a single term, the investigator index is consulted for a match. The investigator (collaborator) index includes full names, if available. Enter a full investigator name in natural or inverted order, e.g., harry janes or janes harry.

5. If no match is found?

PubMed breaks apart the phrase and repeats the above automatic term mapping process until a match is found. PubMed ignores stopwords in searches.

If there is no match, the individual terms will be combined (ANDed) together and searched in all fields.

When a search includes terms that were tagged with a search field during the automatic term mapping process and retrieves zero results, the system triggers a subsequent search using "Schema: all ." "Schema: all" modifies the search by removing the automatically added search field tags, and then searches each term in all fields.

The learned ranking algorithm combines over 150 signals that are helpful for finding best matching results. Most of these signals are computed from the query-document term pairs (e.g., number of term matches between the query and the document) while others are either specific to a document (e.g., publication type; publication year) or query (e.g., query length). The new ranking model was built on relevance data extracted from the anonymous and aggregated PubMed search logs over an extended period of time.

For more information about the Best Match algorithm, please see:

Technical details in the paper Best Match: New relevance search for PubMed by Fiorini N, Canese K, Starchenko G, et al. in PLoS Biol (2018).
NLM Technical Bulletin article: Updated Algorithm for the PubMed Best Match Sort Order

The PubMed database contains citations and abstracts to biomedical literature, facilitating searching across several NLM literature resources:

PubMed Central (PMC)

Ncbi bookshelf.

For additional information, please see the NLM Fact Sheet: Medline, PubMed, and PMC (PubMed Central): How are they different?

PubMed includes citations to original research articles, literature reviews, case reports, letters, editorials, commentaries, and other selected publications on scientific and medical topics (see: publication types found in PubMed ). Some categories of content are out of scope for PubMed, such as: book reviews, individual conference abstracts, obituaries and in memoriam articles , news and announcements, and brief summaries of research articles. More examples are included in XML Help for PubMed Data Providers: What types of articles are accepted? .

MEDLINE contains citations to journal articles in the life sciences with a concentration on biomedicine. The MEDLINE database contains citations from the late 1940s to the present , with some older material.

New citations from MEDLINE journals are received electronically from publishers and appear in PubMed daily. Most citations progress to in-process, and then to indexed for MEDLINE; however, not all citations will be indexed for MEDLINE. PubMed includes some citations from MEDLINE journals that are not indexed for MEDLINE, such as:

Citations preceding the date that a journal was selected for MEDLINE indexing.
Out-of-scope citations (e.g., articles on plate tectonics or astrophysics) from certain MEDLINE journals, primarily general science and chemistry journals, for which the life sciences articles are indexed for MEDLINE.

Citations that have been indexed for MEDLINE and updated with NLM Medical Subject Headings (MeSH) , publication types, GenBank accession numbers, and other indexing data are available daily. To limit your search to MEDLINE citations, add medline[sb] to your search.

Indexing method

The method used to assign Medical Subject Headings (MeSH) has changed over time. For more information, please see Incorporating Values for Indexing Method in MEDLINE/PubMed XML . Use the following searches to find citations indexed with each method:

Automated - MeSH indexing is provided algorithmically. Search: indexingmethod_automated
Curated - MeSH indexing is provided algorithmically and a human reviewed (and possibly modified) the algorithm results. Search: indexingmethod_curated
Fully human indexed – Search: medline[sb] NOT (indexingmethod_curated OR indexingmethod_automated)

PubMed Central (PMC) is a full text archive that includes articles from journals reviewed and selected by NLM for archiving (current and historical), as well as individual articles and preprints collected for archiving in compliance with funder policies. Some PMC content is not cited in PubMed, such as book reviews and conference abstracts (see: PubMed coverage ).

As of June 2020, PubMed Central (PMC) includes preprints that report NIH-funded research results. Citations to these preprints are deposited in PubMed. To learn more, see: NIH Preprint Pilot .

To search for preprints in PubMed, include preprint[filter] in your query.

To exclude preprints from your search results in PubMed, use the Boolean operator NOT.

Bookshelf is a full text archive of books, reports, databases, and other documents related to biomedical, health, and life sciences. PubMed includes citations for books and some individual chapters available on Bookshelf.

The PubMed Format tags table defines the data tags that compose the PubMed format. The tags are presented in alphabetical order. Some of the tags (e.g., CIN) are not mandatory and therefore will not be found in every record. Other tags (e.g., AU, MH, and RN) may occur multiple times in one record. You can download records in PubMed format as a text file (.txt) or as an .nbib file for exporting into citation management software programs .

Not all fields are searchable in PubMed. See Search field tags for the list of searchable fields.

This documentation describes the fields found in PubMed records. If a field is searchable, the search tag appears after the field name in square brackets: Affiliation [ad]. A small number of searchable fields do not correspond to a specific field in the PubMed format .

See Search field tags for a list of searchable fields.
See PubMed format for a quick table view of the fields found in PubMed records.

Affiliation may be included for authors, corporate authors and investigators, e.g., cleveland [ad] AND clinic [ad], if submitted by the publisher.

Multiple affiliations were added to citations starting from 2014, previously only the first author’s affiliation was included. PubMed includes the note "Contributed equally" in the affiliation field when this information is supplied by publishers.

Searching for terms in the affiliation field searches in all author affiliations on a citation. For example, a search for Hopkins[ad] AND Bloomberg[ad] can find these terms spread across multiple authors’ affiliations on the same citation.

To search for multiple terms appearing within the same affiliation, use a proximity search . You can also search affiliations using a phrase search ; however, we suggest using a proximity search for more comprehensive results because affiliation data may be provided in a variety of ways for the same institution.

Use proximity searching to find citations with authors from the Johns Hopkins Bloomberg School of Public Health:

This search will find any citation where the words "Hopkins," "Bloomberg," and "Public" appear in the same affiliation, with no more than forty-five words between each term. Search results may include:

Johns Hopkins Bloomberg School of Public Health
Johns Hopkins University, Bloomberg School of Public Health
Bloomberg School of Public Health, Johns Hopkins University
Bloomberg Johns Hopkins University School of Public Health
...and more!

Untagged terms and terms tagged with [all] are processed using Automatic Term Mapping (ATM) . Terms that do not map are searched in all search fields except for Place of Publication, Create Date, Completion Date, Entry Date, MeSH Date, and Modification Date. Terms enclosed in double quotes or truncated will be searched in all fields and not processed using automatic term mapping. PubMed ignores stopwords .

Includes article identifiers submitted by journal publishers such as DOI (digital object identifier).

The format to search for this field is: last name followed by a space and up to the first two initials followed by a space and a suffix abbreviation, if applicable, all without periods or a comma after the last name (e.g., fauci as or o'brien jc jr). Initials and suffixes may be omitted when searching.

PubMed automatically truncates a search for an author's name to account for varying initials, e.g., o'brien j [au] will retrieve o'brien ja, o'brien jb, o'brien jc jr, as well as o'brien j. To turn off automatic truncation, enclose the author's name in double quotes and tag with [au] in brackets, e.g., "o'brien j" [au] to retrieve just o'brien j.

Searching by full author name for articles published from 2002 forward is also possible, if available. See NLM policy on author names .

The author identifier includes a unique identifier associated with an author, corporate or investigator name, if supplied by a publisher. The field includes the organization authority that established the unique identifier, such as, ORCID, ISNI, VIAF, e.g., orcid 0000-0001-5027-4446 [auid].

The book search field includes book citations, e.g., genereviews [book].

Use the following untagged searches to retrieve all book or book chapters, e.g., ataxia AND pmcbookchapter

books and chapters: pmcbook
books: pmcbooktitle
book chapters: pmcbookchapter

The above searches capture book records provided by the NCBI Bookshelf database; they exclude a small number of documents from other providers that appear in both PubMed and Bookshelf. For the most comprehensive search of records appearing in both PubMed and Bookshelf, search "pubmed books"[sb].

The data in these fields are citations to other associated journal publications, e.g., comments or errata. Often these link to the respective citation. Comments/Corrections data can be retrieved by the search term that follows each type:

Comment in: hascommentin
Comment on: hascommenton
Corrected and republished in: hascorrectedrepublishedin
Corrected and republished from: hascorrectedrepublishedfrom
Dataset use reported in: hasassociatedpublication
Dataset described in: hasassociateddataset
Erratum in: haserratumin
Erratum for: haserratumfor
Expression of concern in: hasexpressionofconcernin
Expression of concern for: hasexpressionofconcernfor
Original Report in: hasoriginalreportin
Republished in: hasrepublishedin
Republished from: hasrepublishedfrom
Retracted and republished in: hasretractedandrepublishedin
Retracted and republished from: hasretractedandrepublishedfrom
Retraction in: hasretractionin
Retraction of: hasretractionof
Summary for patients in: hassummaryforpatientsin
Update in: hasupdatein
Update of: hasupdateof

Used by NLM for internal processing. Completon Date is not included in All Fields retrieval; the [dcom] search tag is required.

The conflict of interest statement from the published article. Conflict of interest statements are available when supplied by the publisher in the citation data sent to PubMed, or when included in full text articles in PubMed Central (PMC).

To retrieve all citations that contain conflict of interest statements, use the query hascois.

Corporate author identifies the corporate or collective authorship of an article. Corporate names display exactly as they appear in the journal.

Note: Citations indexed pre-2000 and some citations indexed in 2000-2001 retain corporate authors at the end of the title field. For comprehensive searches, consider including terms and/or words searched in the title field [ti].

The date the citation record was first created in PubMed. Create Date can be helpful when checking PubMed for citations added since the last time a query was run. Create Date is not included in All Fields retrieval; the [crdt] search tag is required.

EC/RN numbers are assigned by:

The Food and Drug Administration (FDA) Substance Registration System for Unique Ingredient Identifiers (UNIIs), e.g., Y92OUS2H9B
The Enzyme Commission (EC) to designate a particular enzyme, e.g., EC 1.1.1.57
The Chemical Abstracts Service (CAS) for Registry Numbers, e.g., 2751-14-6

The EC/RN number search field includes both the Registry Number and the Related Registry Number (available in the NLM MeSH Browser).

The editor search field includes the editors for book or chapter citations.

Entry date (EDAT) is used for PubMed processing, such as “Most Recent” sort order (i.e., last in, first out).

EDAT is typically set within 24 hours of the citation’s availability in PubMed. Exceptions: As of December 15, 2008, citations added to PubMed more than twelve months after the date of publication have the EDAT set to the date of publication, except for book citations. Prior to this, the Entry Date was set to the Publication Date on citations published before September 1997. Entry Date is not included in All Fields retrieval; the [edat] search tag is required.

Note: Entry Date was called Entrez Date in the legacy PubMed system (retired in 2020).

Technical tags used by LinkOut, filters include:

loall[sb] - citations with LinkOut links in PubMed
free full text[sb] - citations that include a link to a free full text article
full text[sb] - citations that include a link to a full text article

The first personal author name in a citation.

The full author name for articles published from 2002 forward, if available. Full author searches can be entered in natural or inverted order, e.g., julia s wong or wong julia s.

The index for the article's full investigator or collaborator name, if available. Full investigator searches can be entered in natural or inverted order, e.g., harry janes or janes harry.

The Grants and funding [gr] search field (previously Grant Number) includes grant numbers, contract numbers, or other intramural research identifiers associated with a publication.

The most common type of funding information associated with a publication in PubMed are grant numbers. Data in the Grants and funding search field can consist of up to four parts:

Number contains the grant, contract, intramural project number (or both) that designates financial support by any agency of the United States Public Health Service (US PHS), any institute of the National Institutes of Health, or other organization.
Funder code contains the 2-letter grant code or funding organization acronym, for example: CA for National Cancer Institute or DDCF for Doris Duke Charitable Foundation. See Grant Number Information Found in the GR Field in MEDLINE/PubMed (Archived) for the 2-character abbreviations, PHS agency acronyms, and other US and non-US funding organizations.
Agency includes the acronym or mnemonic in the case of US PHS agencies, or full organization name. As of 2009 this includes the agency's hierarchical structure from lower to higher entity, when known. For example, NCI NIH HHS for National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services.
Country contains the home country of the funding agency, for example: United States.

Each individual part can be searched using [gr], for example: CA101211[gr], CA[gr], NCI[gr], NIH[gr], or United States[gr].

This field can also be searched to find articles with intramural support; e.g., "intramural nih"[gr] finds all journal citations authored by intramural NIH staff.

Completeness of funding information in PubMed will vary by source.

See Grants and funding for more information about data in this field.

Names of principal investigator(s) or collaborators who contributed to the research. Search names following the author field format, for example: soller b[ir].

The ISBN for book or book chapters.

The number of the journal issue in which the article was published.

The journal search field includes the journal title abbreviation, full journal title, or ISSN/eISSN number (e.g., J Biol Chem, Journal of Biological Chemistry, 0021-9258). If a journal title contains special characters, e.g., parentheses, brackets, enter the name without these characters, e.g., enter J Hand Surg [Am] as J Hand Surg Am.

The language search field includes the language in which the article was published. Note that many non-English articles have English language abstracts. You may search using either the language or the first three characters of most languages, e.g., chi [la] retrieves the same results as chinese [la]. The most notable exception is jpn [la] for Japanese.

The last personal author name in a citation.

Location ID includes the DOI or publisher ID that serves the role of pagination to locate an online article.

The date the citation was indexed with MeSH Terms and elevated to MEDLINE for citations with an Entry Date after March 4, 2000. The MeSH Date is initially set to the Entry Date when the citation is added to PubMed. MeSH Date is not included in All Fields retrieval; the [mhda] search tag is required.

Dates must be entered using the format YYYY/MM/DD [mhda], e.g., 2000/03/15 [mhda]. The month and day are optional (e.g., 2000 [mhda] or 2000/03 [mhda]).

To enter a date range, insert a colon (:) between each date, e.g., 1999:2000 [mhda] or 2000/03:2000/04 [mhda].

A MeSH term that is one of the main topics discussed in the article denoted by an asterisk on the MeSH term or MeSH/Subheading combination, e.g., Cytokines/physiology* See MeSH Terms [mh] below.

MeSH Subheadings are used with MeSH terms to help describe more completely a particular aspect of a subject. For example, the drug therapy of asthma is displayed as asthma/drug therapy; see MeSH/Subheading Combinations in MeSH Terms [mh] below.

The MeSH Subheading field allows users to "free float" Subheadings, e.g., hypertension [mh] AND toxicity [sh].

MeSH Subheadings automatically include the more specific Subheading terms under the term in a search. To turn off this automatic feature, use the search syntax [sh:noexp], e.g., therapy [sh:noexp].

In addition, you can enter the two-letter MeSH Subheading abbreviations rather than spelling out the Subheading, e.g., dh [sh] = diet therapy [sh].

The NLM Medical Subject Headings controlled vocabulary of biomedical terms that is used to describe the subject of each journal article in MEDLINE. MeSH is updated annually to reflect changes in medicine and medical terminology. MeSH terms are arranged hierarchically by subject categories with more specific terms arranged beneath broader terms. PubMed allows you to view this hierarchy and select terms for searching in the MeSH Database.

MEDLINE articles are automatically indexed with MeSH terms using a well-refined algorithm. Applying the MeSH vocabulary ensures that articles are uniformly indexed by subject, whatever the author's words. For more information, see Frequently Asked Questions about Indexing for MEDLINE .

More information about MeSH Terms and Major MeSH Topic search fields:

To search the term only as a MeSH term, it must be tagged using the search field, e.g., [mh] for MeSH Terms or [majr] for MeSH Major Topic. A tagged term is checked against the subject translation table , and then mapped to the appropriate MeSH term(s). To turn off mapping to multiple MeSH terms, enter the tagged MeSH term in double quotes.
MeSH terms are arranged hierarchically by subject categories with more specific terms arranged beneath broader terms. MeSH terms in PubMed automatically include the more specific MeSH terms in a search. To turn off this automatic feature, use the search syntax [mh:noexp], e.g., neoplasms [mh:noexp].For more detailed information about MeSH vocabulary including the hierarchical structure, please see the MeSH homepage .
MeSH/Subheading Combinations: To directly attach MeSH Subheadings, use the format MeSH Term/Subheading, e.g., neoplasms/diet therapy. You may also use the two-letter MeSH Subheading abbreviations , e.g., neoplasms/dh. The [mh] tag is not required, however [majr] may be used, e.g., plants/genetics[majr]. Only one Subheading may be directly attached to a MeSH term. For a MeSH/Subheading combination, PubMed always includes the more specific terms arranged beneath broader terms for the MeSH term and also includes the more specific terms arranged beneath broader Subheadings . The broader Subheading, or one of its indentions, will be directly attached to the MeSH term or one of its indentions. For example, hypertension/therapy also retrieves hypertension/diet therapy; hypertension/drug therapy; hypertension, malignant/therapy; hypertension, malignant/drug therapy, and so on, as well as hypertension/therapy.
To turn off the automatic inclusion of the more specific terms, use the syntax [field:noexp], e.g., hypertension [mh:noexp], or hypertension [majr:noexp], or hypertension/therapy [mh:noexp]. The latter example turns off the more specific terms in both parts, searching for only the one Subheading therapy attached directly to only the one MeSH term hypertension.
If parentheses are embedded in a MeSH term, replace the parentheses with a space and tag with [mh] e.g., enter the MeSH term Benzo(a)pyrene as benzo a pyrene [mh].
MeSH terms can be selected for searching in the MeSH database and from the advanced search builder index.

Modification date is a completed citation’s most recent revision date. Modification Date is not included in All Fields retrieval; the [lr] search tag is required.

The NLM ID is the alpha-numeric identifier for the cited journal that was assigned by the NLM Integrated Library System LocatorPlus, e.g., 0375267 [jid].

The author keyword field (OT field) is searchable with the title/abstract [tiab], text word [tw] and other term [ot] search tags. To retrieve all citations that have keywords, use the query haskeyword. Other term data may display an asterisk to indicate a major concept; however, you cannot search other terms with a major concept tag.

The owner search field includes the acronym that identifies the organization that supplied the citation data. Search using owner + the owner acronym, e.g., ownernasa.

Enter only the first page number that the article appears on. The citation will display the full pagination of the article but this field is searchable using only the first page number.

Use this search field tag to limit retrieval to where the name is the subject of the article, e.g., varmus h[ps]. Search for personal names as subject using the author field format, e.g., varmus h[ps].

Substances known to have a particular pharmacologic action. Each pharmacologic action term index is created with the drug/substance terms known to have that effect. This includes both MeSH terms and terms for Supplementary Concept Records.

Indicates the cited journal's country of publication. Geographic place of publication regions are not searchable. In order to retrieve records for all countries in a region (e.g., North America) it is necessary to OR together the countries of interest. Note: This field is not included in all fields or text word retrieval.

Search for PMC or NIH manuscript identifiers using the appropriate prefix followed by the ID number, e.g., PMC2600426. To retrieve all NIH manuscript citations, use the query hasnihmsid.

To search for a PubMed Identifier (PMID), enter the ID with or without the search field tag [pmid]. You can search for several PMIDs by entering each number in the search box separated by a space (e.g., 17170002 16381840); PubMed will OR the PMIDs together.

PMIDs do not change over time or during processing and are never reused.

Publication date is the date that the article was published. The search field tags [dp] and [pdat] may be used interchangeably for publication date searching.

Dates or date ranges must be searched using the format yyyy/mm/dd [dp], e.g., 1998/03/06 [dp]. The month and day are optional (e.g., 1998 [dp] or 1998/03 [dp]).

To enter a date range search, insert a colon (:) between each date, e.g., 1996:1998 [dp] or 1998/01:1998/04 [dp].

Use the following format to search X days, months or years immediately preceding today’s date where X = numeric value:

"last X days"[dp]
"last X months"[dp]
"last X year"[dp]

More information about publication dates:

The time between an article’s publication and the citation’s availability in PubMed varies depending on when the publisher deposits the citation to PubMed. Because of this, searching with Create Date [crdt] (the date a citation was created in PubMed) is often more comprehensive than Publication Date [dp] when checking PubMed on a regular basis for new citations.
Journals vary in the way the publication date appears on an issue. Some journals include just the year, whereas others include the year plus month or year plus month plus day. And, some journals use the year and season (e.g., Winter 1997). The publication date in the citation is recorded as it appears in the journal.
Publication dates without a month are set to January, multiple months (e.g., Oct-Dec) are set to the first month, and dates without a day are set to the first day of the month. Dates with a season are set as: winter = January, spring = April, summer = July and fall = October.
If an article is published electronically and in print on different dates both dates are searchable and may be included on the citation prefaced with an Epub or Print label. The electronic date will not be searchable if it is later than the print date, except when range searching.
To search for electronic dates only use the search tag [EPDAT], for print dates only tag with [PPDAT].
Most journals now publish articles online on a continuous basis, as soon as they are ready for publication (after peer review and editing, etc.) instead of, or in addition to, publishing collections of articles as an "issue" on a periodic basis. When a journal deposits a citation for an "online first" article in PubMed, NLM appends the note "[Online ahead of print]" to the online publication date. The citation is updated, and the ahead of print notation removed, when the article is included in a journal issue. The lag between the "online first" and "issue" publication dates may be days, weeks, months, or more than a year. In many cases, depending on the journal, the online first version is considered to be the version of record. The "[Online ahead of print]" note in PubMed should not be taken to mean that the cited article is not the version of record.
Bookshelf citation publication dates are generated from the book’s publication date.

Describes the material presented in the article (e.g., Review, Clinical Trial, Retracted Publication, Letter). Citations may include multiple Publication Types. Use the search tag [pt] with any PubMed Publication Type , e.g., review[pt].

Publication Types are arranged hierarchically with more specific terms arranged beneath broader terms, and publication types automatically include the more specific publication types in a search. To turn off this automatic feature, use the search syntax [pt:noexp], e.g., review [pt:noexp].

Includes publisher names for Bookshelf citations.

The SI field identifies secondary source databanks and accession numbers, e.g., GenBank, GEO, PubChem, ClinicalTrials.gov , ISRCTN. The field is composed of the source followed by a slash followed by an accession number and can be searched with one or both components, e.g., genbank [si], AF001892 [si], genbank/AF001892 [si]. To retrieve all citations with an SI value, search hasdatabanklist.

The subset field is a method of restricting retrieval by subject, citation status and journal category, with the search tag [SB]. See also filters and Find related resources using LinkOut.

Includes chemical, protocol, disease or organism terms. Synonyms to the supplementary concepts will automatically map when tagged with [nm]. This field was implemented in mid-1980; however, many chemical names are searchable as MeSH terms before that date.

Includes all words and numbers in the title, abstract, other abstract, MeSH terms, MeSH Subheadings, Publication Types, Substance Names, Personal Name as Subject, Corporate Author, Secondary Source, Comment/Correction Notes, and Other Terms (see Other Term [OT] above) typically non-MeSH subject terms (keywords), including NASA Space Flight Mission, assigned by an organization other than NLM.

Words and numbers included in the title of a citation, as well as the collection title for book citations.

Words and numbers included in a citation's title, collection title, abstract, other abstract and author keywords ( Other Term [ot] field). English language abstracts are taken directly from the published article. If an article does not have a published abstract, NLM does not create one.

Words and numbers in title originally published in a non-English language, in that language. Non-Roman alphabet language titles are transliterated. Transliterated title is not included in Text Word [TW] retrieval.

The number of the journal volume in which an article is published.

NLM author indexing policy is as follows:

1966 - 1984: MEDLINE did not limit the number of authors.
1984 - 1995: The NLM limited the number of authors to 10, with "et al" as the eleventh occurrence.
1996 - 1999: The NLM increased the limit from 10 to 25. If there were more than 25 authors, the first 24 were listed, the last author was used as the 25th, and the twenty-sixth and beyond became "et al."
2000 - Present: MEDLINE does not limit the number of authors.

More information:

Beginning in mid-2005, the policy restrictions on number of author names in past years were lifted so that on an individual basis, a citation may be edited to include all author names in the published article, regardless of the limitation in effect when the citation was created.
Effective with 1992 date of publication, letters are indexed individually with authors rather than as an anonymous group.
Until 1990, NLM transliterated up to five authors' Cyrillic or Japanese names to the Roman alphabet.
Between 1990 and 2016, the first ten Cyrillic or Japanese names are transliterated. Chinese ideograms were not transliterated by NLM, but if transliterations of the authors names are available in the journal article or table of contents, they were included in the citation, even if that includes only one author in a multi-author article.
Beginning in 2016, author names are published in Roman characters in all MEDLINE journals, and NLM no longer transliterates Cyrillic or Japanese names. All author names are included as published.

System error messages

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Typographical errors

Please contact the journal publisher directly to report an error and initiate a correction to PubMed citations for content other than MeSH.

To report a MeSH error in a PubMed citation, please contact the NLM Help Desk and include the PMID number (e.g., PMID: 12345678), and an indication of the incorrect and correct information.

NLM provides data to vendors around the world. Other products and services will not necessarily immediately reflect corrections made to PubMed records. If you search through a vendor's system, please contact your vendor about their maintenance schedules.

A "cookie" is information stored by a web site server on your computer. See the NLM Privacy Policy for additional information.

In the case of PubMed, cookies store information about your interactions that may be needed later to perform a function. To use these interactive features you need to enable cookies on your computer. Consult your browser's help for information on enabling cookies.

If you have problems using cookie-dependent features of PubMed, even after enabling cookies, possible reasons may include:

Cookies are blocked by your provider or institution. Check with your Internet provider and/or the system administrator at your institution to see if cookies can be accepted. Even if you have them enabled in your web browser, if they are blocked by your provider or institution (e.g., by a firewall, proxy server, etc.), cookie-dependent features of PubMed won't work.
Your computer's date and time settings are incorrect. Check your computer's time settings to ensure that they are correct.

See the MeSH Subheadings table below and scope notes and allowable categories on the NLM website.

Certain characters have special meaning in searches, others are converted to spaces.

Searches that include the following characters are translated as follows:

parentheses ( ) - used to create Boolean nesting
square brackets [ ] - search field tag qualification
ampersand & - Boolean operator AND
pipe | - Boolean operator OR
forward slash / - MeSH/Subheading combinations
colon : - designates a range operation
double quotes " - used to force a phrase search
pound sign # - designates a History search statement when immediately followed by a number, e.g., #1 AND cat
asterisk * - wildcard symbol for search term truncation, e.g., toxicol*

Characters converted to spaces in search queries:

exclamation mark !
pound sign #
dollar sign $
percentage sign %
asterisk * (if it cannot be used in a wildcard search, for example, when a term is too short)
plus symbol +
minus symbol -
semi-colon ;
angle brackets < >
equal sign =
question mark ?
backslash \
underscore _
curly brackets { }
approximately ~
single quotes '

Some characters have special meaning in MeSH fields:

forward slash /

Publication types found in PubMed are listed below. See Publication Type [PT] and MeSH Publication Types with Scope Notes for more information; however, not all MeSH Publication Types are included in PubMed.

Adaptive Clinical Trial
Autobiography
Bibliography
Case Reports
Classical Article
Clinical Conference
Clinical Study
Clinical Trial
Clinical Trial, Phase I
Clinical Trial, Phase II
Clinical Trial, Phase III
Clinical Trial, Phase IV
Clinical Trial Protocol
Clinical Trial, Veterinary
Collected Work
Comparative Study
Consensus Development Conference
Consensus Development Conference, NIH
Controlled Clinical Trial
Corrected and Republished Article
Duplicate Publication
Electronic Supplementary Materials
English Abstract
Equivalence Trial
Evaluation Study
Expression of Concern
Festschrift
Government Publication
Historical Article
Interactive Tutorial
Introductory Journal Article
Journal Article (Default value when no more descriptive PT is provided or assigned)
Legislation
Meta-Analysis
Multicenter Study
Newspaper Article
Observational Study
Observational Study, Veterinary
Patient Education Handout
Periodical Index
Personal Narrative
Practice Guideline
Pragmatic Clinical Trial
Published Erratum
Randomized Controlled Trial
Randomized Controlled Trial, Veterinary
Research Support, American Recovery and Reinvestment Act
Research Support, N.I.H., Extramural
Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, U.S. Gov't, P.H.S.
Retracted Publication
Retraction of Publication
Scientific Integrity Review
Systematic Review
Technical Report
Validation Study
Video-Audio Media

Article attribute

Most article type filters use the article type name with the publication type [pt] search field tag; for example, "multicenter study"[pt].

The Systematic Review filter uses a search strategy in addition to the publication type [pt].

The Books and Documents filter uses the following query: "pubmed books"[sb].

The article language filters use the language name with the language [la] search field tag; for example, esperanto[la].

See Other filters and more subsets .

COVID-19 article filters

The COVID-19 article filters limit retrieval to citations about the 2019 novel coronavirus; these filters may evolve over time.

The Clinical Study Categories search filters are based on the work of Haynes RB et al.

Clinical Study Categories bibliography

The Clinical Queries search strategies have been updated based on new evidence from Haynes et al. The current strategies have better performance than their predecessors . Details of methods appear in the references below.

Revised December 2011

Wilczynski NL, McKibbon KA, Haynes RB. Sensitive Clinical Queries retrieved relevant systematic reviews as well as primary studies: an analytic survey. J Clin Epidemiol. 2011 Dec;64(12):1341-9. doi: 10.1016/j.jclinepi.2011.04.007. Epub 2011 Jul 19. PMID: 21775104 .
Lokker C, Haynes RB, Wilczynski NL, McKibbon KA, Walter SD. Retrieval of diagnostic and treatment studies for clinical use through PubMed and PubMed's Clinical Queries filters. J Am Med Inform Assoc. 2011 Sep-Oct;18(5):652-9. doi: 10.1136/amiajnl-2011-000233. Epub 2011 Jun 15. PMID: 21680559 ; PMCID: PMC3168323 .
Wilczynski NL, Haynes RB; QI Hedges Team. Optimal search filters for detecting quality improvement studies in Medline. Qual Saf Health Care. 2010 Dec;19(6):e31. doi: 10.1136/qshc.2010.042432. Epub 2010 Jul 29. PMID: 20671080 .
Kastner M, Wilczynski NL, McKibbon AK, Garg AX, Haynes RB. Diagnostic test systematic reviews: bibliographic search filters ("Clinical Queries") for diagnostic accuracy studies perform well. J Clin Epidemiol. 2009 Sep;62(9):974-81. doi: 10.1016/j.jclinepi.2008.11.006. Epub 2009 Feb 20. PMID: 19230607 ; PMCID: PMC2737707 .
Wilczynski NL, Haynes RB. Response to Corrao et al.: Improving efficacy of PubMed clinical queries for retrieving scientifically strong studies on treatment. J Am Med Inform Assoc. 2007 Mar-Apr;14(2):247-8. Epub 2007 Jan 9. PMID: 17213490 ; PMCID: PMC2213472 .
Wilczynski NL, McKibbon KA, Haynes RB. Response to Glanville et al.: How to identify randomized controlled trials in MEDLINE: ten years on. J Med Libr Assoc. 2007 Apr;95(2):117-8; author reply 119-20. PMID: 17443240 ; PMCID: PMC1852612 .
Wilczynski NL, Morgan D, Haynes RB; Hedges Team. An overview of the design and methods for retrieving high-quality studies for clinical care. BMC Med Inform Decis Mak. 2005 Jun 21;5:20. doi: 10.1186/1472-6947-5-20. PMID: 15969765 ; PMCID: PMC1183213 .
Haynes RB, McKibbon KA, Wilczynski NL, Walter SD, Werre SR; Hedges Team. Optimal search strategies for retrieving scientifically strong studies of treatment from Medline: analytical survey. BMJ. 2005 May 21;330(7501):1179. doi: 10.1136/bmj.38446.498542.8F. Epub 2005 May 13. PMID: 15894554 ; PMCID: PMC558012 .
Montori VM, Wilczynski NL, Morgan D, Haynes RB; Hedges Team. Optimal search strategies for retrieving systematic reviews from Medline: analytical survey. BMJ. 2005 Jan 8;330(7482):68. doi: 10.1136/bmj.38336.804167.47. Epub 2004 Dec 24. PMID: 15619601 ; PMCID: PMC543864 .
Wilczynski NL, Haynes RB, Lavis JN, Ramkissoonsingh R, Arnold-Oatley AE; HSR Hedges team. Optimal search strategies for detecting health services research studies in MEDLINE. CMAJ. 2004 Nov 9;171(10):1179-85. doi: 10.1503/cmaj.1040512. PMID: 15534310 ; PMCID: PMC524948 .
Wilczynski NL, Haynes RB; Hedges Team. Developing optimal search strategies for detecting clinically sound prognostic studies in MEDLINE: an analytic survey. BMC Med. 2004 Jun 9;2:23. doi: 10.1186/1741-7015-2-23. PMID: 15189561 ; PMCID: PMC441418 .
Haynes RB, Wilczynski NL. Optimal search strategies for retrieving scientifically strong studies of diagnosis from Medline: analytical survey. BMJ. 2004 May 1;328(7447):1040. doi: 10.1136/bmj.38068.557998.EE. Epub 2004 Apr 8. PMID: 15073027 ; PMCID: PMC403841 .
Bhandari M, Montori VM, Devereaux PJ, Wilczynski NL, Morgan D, Haynes RB; Hedges Team. Doubling the impact: publication of systematic review articles in orthopaedic journals. J Bone Joint Surg Am. 2004 May;86(5):1012-6. PMID: 15118046 .
Wong SS, Wilczynski NL, Haynes RB; Hedges Team. Developing optimal search strategies for detecting clinically relevant qualitative studies in MEDLINE. Stud Health Technol Inform. 2004;107(Pt 1):311-6. PMID: 15360825 .
Montori VM, Wilczynski NL, Morgan D, Haynes RB; Hedges Team. Systematic reviews: a cross-sectional study of location and citation counts. BMC Med. 2003 Nov 24;1:2. doi: 10.1186/1741-7015-1-2. PMID: 14633274 ; PMCID: PMC281591 .
Wong SS, Wilczynski NL, Haynes RB, Ramkissoonsingh R; Hedges Team. Developing optimal search strategies for detecting sound clinical prediction studies in MEDLINE. AMIA Annu Symp Proc. 2003;2003:728-32. PMID: 14728269 ; PMCID: PMC1479983 .
Wilczynski NL, Haynes RB; Hedges Team. Developing optimal search strategies for detecting clinically sound causation studies in MEDLINE. AMIA Annu Symp Proc. 2003;2003:719-23. PMID: 14728267 ; PMCID: PMC1480286 .
Wilczynski NL, McKibbon KA, Haynes RB. Enhancing retrieval of best evidence for health care from bibliographic databases: calibration of the hand search of the literature. Stud Health Technol Inform. 2001;84(Pt 1):390-3. PMID: 11604770 .
Haynes RB, Wilczynski N, McKibbon KA, Walker CJ, Sinclair JC. Developing optimal search strategies for detecting clinically sound studies in MEDLINE. J Am Med Inform Assoc. 1994 Nov-Dec;1(6):447-58. doi: 10.1136/jamia.1994.95153434. PMID: 7850570 ; PMCID: PMC116228 .

Medical genetics search filters

The medical genetics searches were developed in conjunction with the staff of GeneReviews: Genetic Disease Online Reviews at GeneTests, University of Washington, Seattle.

The neighbors of a document are those documents in the database that are the most similar to it. The similarity between documents is measured by the words they have in common, with some adjustment for document lengths. To carry out such a program, one must first define what a word is. For us, a word is basically an unbroken string of letters and numerals with at least one letter of the alphabet in it. Words end at hyphens, spaces, new lines, and punctuation. The 132 common, but uninformative, words (also known as stopwords) are eliminated from processing at this stage. Next, a limited amount of stemming of words is done, but no thesaurus is used in processing. Words from the abstract of a document are classified as text words. Words from titles are also classified as text words, but words from titles are added in a second time to give them a small advantage in the local weighting scheme. MeSH terms are placed in a third category, and a MeSH term with a subheading qualifier is entered twice, once without the qualifier and once with it. If a MeSH term is starred (indicating a major concept in a document), the star is ignored. These three categories of words (or phrases in the case of MeSH) comprise the representation of a document. No other fields, such as Author or Journal, enter into the calculations.

Having obtained the set of terms that represent each document, the next step is to recognize that not all words are of equal value. Each time a word is used, it is assigned a numerical weight. This numerical weight is based on information that the computer can obtain by automatic processing. Automatic processing is important because the number of different terms that have to be assigned weights is close to two million for this system. The weight or value of a term is dependent on three types of information: 1) the number of different documents in the database that contain the term; 2) the number of times the term occurs in a particular document; and 3) the number of term occurrences in the document. The first of these pieces of information is used to produce a number called the global weight of the term. The global weight is used in weighting the term throughout the database. The second and third pieces of information pertain only to a particular document and are used to produce a number called the local weight of the term in that specific document. When a word occurs in two documents, its weight is computed as the product of the global weight times the two local weights (one pertaining to each of the documents).

The global weight of a term is greater for the less frequent terms. This is reasonable because the presence of a term that occurred in most of the documents would really tell one very little about a document. On the other hand, a term that occurred in only 100 documents of one million would be very helpful in limiting the set of documents of interest. A word that occurred in only 10 documents is likely to be even more informative and will receive an even higher weight.

The local weight of a term is the measure of its importance in a particular document. Generally, the more frequent a term is within a document, the more important it is in representing the content of that document. However, this relationship is saturating, i.e., as the frequency continues to go up, the importance of the word increases less rapidly and finally comes to a finite limit. In addition, we do not want a longer document to be considered more important just because it is longer; therefore, a length correction is applied. This local weight computation is based on the Poisson distribution and the formula can be found in Lin J and Wilbur WJ .

The similarity between two documents is computed by adding up the weights (local wt1 × local wt2 × global wt) of all of the terms the two documents have in common. This provides an indication of how related two documents are. The resultant score is an example of a vector score. Vector scoring was originated by Gerard Salton and has a long history in text retrieval. The interested reader is referred to Salton, Automatic Text Processing, Reading, MA: Addison-Wesley, 1989 for further information on this topic. Our approach differs from other approaches in the way we calculate the local weights for the individual terms. Once the similarity score of a document in relation to each of the other documents in the database has been computed, that document's neighbors are identified as the most similar (highest scoring) documents found. These closely related documents are pre-computed for each document in PubMed so that when you select Similar articles, the system has only to retrieve this list. This enables a fast response time for such queries.

PubMed journals

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PubMed and NCBI molecular biology database journals

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

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How to Search in Biomedical Databases

Getting Started with your Search

PubMed Search Tips

Cochrane Search Tips
Web of Science Search Tips
PsycINFO Search Tips
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Why Use PubMed?
How to Access PubMed at NEOMED
Before You Start Searching . . .
Step 1: Identifying Key Search Concepts Using PICO
Step 2: Locating Relevant MeSH Terms
Step 3: Locating Relevant Keywords & Synonyms
Step 4: Combining MeSH & Keywords Pt. 1
Step 5: Combining MeSH & Keywords Pt. 2
Step 6: Combining Search Elements Using AND
Step 7: Applying Filters
Step 8: Accessing Full-Text, ILL & Exporting Results
More Info . . .
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Contact Your Librarian for Help!

We encourage students, researchers, and faculty members throughout NEOMED's campus and at our clinical sites to use this overview of how to search in PubMed for their research and instruction needs.

Contents (click on items to jump ahead)

Overview: When should I use this database?

How to access PubMed at NEOMED

Before you start searching, keep in mind . . .

Keywords

Automatic Term Mapping

How to Find & Use Keywords

Controlled Vocabularies

Medical Subject Headings (MeSH)

Explode vs. No Explode

Subheadings

Combining Searches Using Boolean Operators (OR, AND, NOT)

Applying Filters

How to Access Full Text

More Information

PubMed Tipsheet (pdf)

Overview: when should I use this database?

PubMed comprises more than 27 million citations for biomedical literature from MEDLINE, life science journals, and online books. The public database is maintained by the U.S. National Library of Medicine (NLM) and the National Center for Biotechnology Information, and this tip sheets applies to this public-facing version, not the version of Medline supported by Ovid. It offers a fairly broad overview of existing literature on a particular topic, but it should not be seen as a complete overview.

How to access PubMed at NEOMED

Please note that to access full text for articles located within PubMed, authentication with your NEOMED Library credentials is required both on- and off-campus to PubMed. The NEOMED instance of PubMed can be located from our landing page searchbox or via the following link: PubMed . Do not simply google PubMed; it will not provide NEOMED full text links. Learn more about how to Access Full Text within a specific PubMed record.

Save your search in a document, citation management software (Endnote, Refworks, etc.), and/or the database

By saving your search, your strategy will be reproducible for another time and properly documented.

Explore options and instruction for citation management here , and find tips on how to export results.

To save searches in PubMed, create an NCBI account by clicking on the sign in to NCBI link in the upper-right corner of the screen (sign up for a My NCBI account by clicking here ). Once you complete a search, click on "Create an alert" underneath the search box. From here you can create a search alert or save your search strategy.

Automatic Term Mapping

PubMed uses Automatic Term Mapping (ATM) when you search with keywords. This means that the search terms you type into the search box are automatically mapped to controlled vocabulary (MeSH) terms. To see ATM in action, scroll to the "Search details" box on the left hand side of the results page. Warning: ATM is not always correct. For example, if you search for “cold AND zinc,” PubMed will include the controlled vocabulary for "cold temperatures" in the search.

Using quotes around a phrase or truncation turns off Automatic Term Mapping. The terms are instead searched as keywords.

Keywords — How to Find & Use

Keyword terms can be single words or phrases.

Use quotes around all phrases to ensure that the phrase is searched instead of each word individually. (e.g. “public health”)

For more possible search terms, visit the MeSH (Medical Subject Headings) database and look at the "entry terms" listed for each MeSH record . MeSH is NLM’s controlled vocabulary of biomedical terms used to describe the subject of each journal article in MEDLINE. The entry terms are synonyms, alternate forms, and other closely related terms generally used interchangeably with the preferred term.

Consult controlled vocabularies in other subject databases for additional help. For example, the Embase has a controlled vocabulary called Emtree . Emtree records contain synonym lists similar to the "entry terms" in a MeSH record. The Emtree synonym list often contains European spellings/variations.

Controlled Vocabularies -- How to Find & Use

Locate controlled vocabulary (mesh).

MeSH (Medical Subject Headings) is NLM’s controlled vocabulary of biomedical terms used to describe the subject of each journal article in MEDLINE. These are a standardized set of terms that are used to bring consistency to the searching process. In total, there are approximately 26,000 terms, and they are updated annually to reflect changes in medicine and medical terminology. Using MeSH terms helps account for variations in language, acronyms, and British vs. American English.

MeSH can be searched from a NCBI interface: https://www.ncbi.nlm.nih.gov/mesh

Terms are arranged hierarchically by subject categories with more specific terms arranged beneath broader terms. MeSH terms in PubMed automatically include the more specific MeSH terms in a search.

To turn off this automatic explode feature, click on the button next to, "Do not include MeSH terms found below this term in the MeSH hierarchy" in the MeSH record or type [mh:noexp] next to the search term, e.g. neoplasms [mh:noexp]. See next page for additional information on no explode.

Once MeSH terms have been searched, terms will appear in a box labelled “Search details,” located beside the list of the results on the right side of the screen. This box will display how each term has been searched, and can be useful for editing your search. Corrections can be made directly within this box, and once corrections have been made, the search button beneath the box will re-run your search.

Difference between “Explode,” “No Explode,” and “Major Heading”

“Explode” will search with all subheadings beneath the main heading included and bring up all results listing any of these terms subject heading subheadings combinations. PubMed will default to explode any MeSH you search.

Choosing to focus (also referred to as “not exploding”) will only search for your chosen MeSH term. Terms are chosen by MeSH indexers to be the primary focus of an individual article. Command to search: [Mesh:noexp] will only find the term specified, not the terms beneath it (for example: “diarrhea”[Mesh:noexp] only finds records indexed with diarrhea, not acute diarrhea or bloody diarrhea, etc.)

Searching for “major headings” will narrow your search to only find MeSH terms listed as a major topic of an article. Command to search: [majr] (e.g. “diarrhea”[majr] will find articles with diarrhea as a major topic. Major topic MeSH terms will have an asterisks (e.g. Diarrhea*), while non-major topics will not have one.

MeSH can be made more specific by the addition of subheadings such as "therapy" and "prevention and control"

When in the MeSH record, add subheadings by clicking on the boxes next to the desired subheadings. Then click "Add to Search Builder." Warning: Adding too many subheadings may lead to missing important articles.

MeSH/Subheading Combinations: You can manually add subheadings in the search box by using the format MeSH Term/Subheading, e.g. neoplasms/diet therapy. You can also use the two letter abbreviation for subheadings rather than typing out the full phrase, e.g. neoplasms/dh. Click here for the abbreviations of other MeSH subheadings. ( https://www.ncbi.nlm.nih.gov/books/NBK3827/table/pubmedhelp.T.mesh_subheadings/ )

For a MeSH/Subheading combination, only one Subheading at a time may be directly attached to a MeSH term. For example, a search of hypertension with the subheadings diagnosis or drug therapy will appear as hypertension/diagnosis or hypertension/drug therapy.

As with MeSH terms, PubMed search results, by default, include the more specific terms arranged beneath broader terms for the MeSH term and also includes the more specific terms arranged beneath broader Subheadings .

Combining Searches Using Boolean Operators

A comprehensive and systematic search of PubMed includes both controlled vocabulary and keyword terms (i.e. free text, natural language, and synonyms).

Boolean operators are used to combine search terms. In PubMed, you can use the operators AND, OR, and NOT.

Go to the “Advanced Search” page to combine searches. This is where your search history is located during your search session.

Boolean operators MUST be used as upper case (AND, OR, NOT).

OR --use OR between similar keywords, like synonyms, acronyms, and variations in spelling within the same idea or concept

AND —use AND to link ideas and concepts where you want to see both ideas or concepts in your search results

NOT —used to exclude specific keywords from the search, however, you will want to use NOT with caution because you may end up missing something important.

You can use field tags to specify where the database looks for the search term. In PubMed, first type the search term and then the field tag in brackets. e.g. Cardiology [TIAB] looks for cardiology in the title and abstract.

[All Fields] or [ALL] – Untagged terms and terms tagged with [all fields] are processed using Automatic Term Mapping . Terms enclosed in double quotes or truncated will be searched in all fields and not processed using automatic term mapping.

[Text Words] or [TW] – Includes all words and numbers in the title, abstract, other abstract, MeSH terms, MeSH Subheadings, Publication Types, Substance Names, Personal Name as Subject, Corporate Author, Secondary Source, Comment/Correction Notes, and Other Terms.

[Title/Abstract] or [TIAB] – Words and numbers included in the title, collection title, abstract, and other abstract of a citation. English language abstracts are taken directly from the published article. If an article does not have a published abstract, NLM does not create one.

NCBI explanation of Field Descriptions and Tags

Applying Filters

On the left side of the results are options to filter your search by Article types, Publication dates, Language, Age, Gender, etc. To access the complete list of filters, click on the “Show additional filters” link.

Use the PubMed built-in limits cautiously. Limits other than date or language will limit your search to indexed records only. In most cases it is best to develop another concept to use as a limiter.

For example, if you would like to limit your results to "human studies," use the following search to exclude animal studies instead of using the "humans" limit from the search results page. Simply add this to the rest of your search strategy using the NOT Boolean operator

(animals[MeSH Terms] NOT humans[MeSH Terms])

In PubMed you can use a * at the root of a word to find multiple endings. For example:

arthroplast* will return arthroplasty, arthroplasties, arthroplastic, arthroplastics, etc.

mobili* will return mobility, mobilization, mobilisation, mobilize, etc.

Note: In PubMed you cannot combine phrase searching with truncation. Either use quotes, e.g. " early childhood mobility ," or use truncation, e.g. early childhood mobili*

In PubMed, the “Northeast Ohio Medical University” icon (pictured above) will often appear within an item record. To access the full text, click the pictured icon to go to an external page listing available full-text options. If the full text is not available, you will see a heading that says, "ILLiad - Request this item through interlibrary loan." When prompted, enter your ILLiad login and password and then submit the request via the pre-filled in template. The article will be emailed to you free of charge (only available for NEOMED students, faculty, and staff).

General principles on searching in any database

PubMedTutorials

Additional tips on exploring journal table of contents, subject filters, and topic alerts

Detailed information about MeSH ( https://www.nlm.nih.gov/mesh/intro_retrieval.html )

This content was adapted from “PubMed Search Tips” by Simon Robins, which is licensed under Creative Commons 4.0 License, CC BY , and content found on Welch Medical Library's Nursing Resources Guide which is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License attributable to the Welch Medical Library

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Last Updated: Feb 13, 2024 11:08 AM
URL: https://libraryguides.neomed.edu/library-tutorials

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Literature Searching

In this guide.

Introduction
Steps for searching the literature in PubMed
Step 1 - Formulate a search question
Step 2- Identify primary concepts and gather synonyms
Step 3 - Locate subject headings (MeSH)
Step 4 - Combine concepts using Boolean operators
Step 5 - Refine search terms and search in PubMed
Step 6 - Apply limits

Steps for Searching the Literature

Searching is an iterative process and often requires re-evaluation and testing by adding or changing keywords and the ways they relate to each other. To guide your search development, you can follow the search steps below. For more information on each step, navigate to its matching tab on the right menu.

1. Formulate a clear, well-defined, answerable search question

Generally, the basic literature search process begins with formulating a clear, well-defined research question. Asking the right research question is essential to creating an effective search. Your research question(s) must be well-defined and answerable. If the question is too broad, your search will yield more information than you can possibly look through.

2. Identify primary concepts and gather synonyms

Your research question will also help identify the primary search concepts. This will allow you to think about how you want the concepts to relate to each other. Since different authors use different terminology to refer to the same concept, you will need to gather synonyms and all the ways authors might express them. However, it is important to balance the terms so that the synonyms do not go beyond the scope of how you've defined them.

3. Locate subject headings (MeSH)

Subject databases like PubMed use 'controlled vocabularies' made up of subject headings that are preassigned to indexed articles that share a similar topic. These subject headings are organized hierarchically within a family tree of broader and narrower concepts. In PubMed and MEDLINE, the subject headings are called Medical Subject Headings (MeSH). By including MeSH terms in your search, you will not have to think about word variations, word endings, plural or singular forms, or synonyms. Some topics or concepts may even have more than one appropriate MeSH term. There are also times when a topic or concept may not have a MeSH term.

4. Combine concepts using Boolean operators AND/OR

Once you have identified your search concepts, synonyms, and MeSH terms, you'll need to put them together using nesting and Boolean operators (e.g. AND, OR, NOT). Nesting uses parentheses to put search terms into groups. Boolean operators are used to combine similar and different concepts into one query.

5. Refine search terms and search in PubMed

There are various database search tactics you can use, such as field tags to limit the search to certain fields, quotation marks for phrase searching, and proximity operators to search a number of spaces between terms to refine your search terms. The constructed search string is ready to be pasted into PubMed.

6. Apply limits (optional)

If you're getting too many results, you can further refine your search results by using limits on the left box of the results page. Limits allow you to narrow your search by a number of facets such as year, journal name, article type, language, age, etc.

Depending on the nature of the literature review, the complexity and comprehensiveness of the search strategies and the choice of databases can be different. Please contact the Lane Librarians if you have any questions.

The type of information you gather is influenced by the type of information source or database you select to search. Bibliographic databases contain references to published literature, such as journal articles, conference abstracts, books, reports, government and legal publications, and patents. Literature reviews typically synthesis indexed, peer-reviewed articles (i.e. works that generally represent the latest original research and have undergone rigorous expert screening before publication), and gray literature (i.e. materials not formally published by commercial publishers or peer-reviewed journals). PubMed offers a breadth of health sciences literature and is a good starting point to locate journal articles.

What is PubMed?

PubMed is a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. Available to the public online since 1996, PubMed was developed and is maintained by the National Center for Biotechnology Information (NCBI) , at the U.S. National Library of Medicine (NLM) , located at the National Institutes of Health (NIH) .

MEDLINE is the National Library of Medicine’s (NLM) premier bibliographic database that contains more than 27 million references to journal articles from more than 5,200 worldwide journals in life sciences with a concentration on biomedicine. The Literature Selection Technica Review Committee (LSTRC) reviews and selects journals for MEDLINE based on the research quality and impact of the journals. A distinctive feature of MEDLINE is that the records are indexed with NLM Medical Subject Headings (MeSH).

PubMed also contains citations for PubMed Central (PMC) articles. PMC is a full-text archive that includes articles from journals reviewed and selected by NLM for archiving (current and historical), as well as individual articles collected for archiving in compliance with funder policies. PubMed allows users to search keywords in the bibliographic data, but not the full text of the PMC articles.

how to search for research articles in pubmed

How to Access PubMed?

To access PubMed, go to the Lane Library homepage and click PubMed in "Top Resources" on the left. This PubMed link is coded with Find Fulltext @ Lane Library Stanford that links you to Lane's full-text articles online.

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Next: Step 1 - Formulate a search question >>
Last Updated: Jan 9, 2024 10:30 AM
URL: https://laneguides.stanford.edu/LitSearch

UNC Libraries
HSL Academic Process
Searching PubMed
Basic Searches

Searching PubMed: Basic Searches

Created by health science librarians.

Topic Search

Author name search, journal search, single citation matcher, advanced search, adding truncation to your search, proximity search.

Filters and Narrowing Searches
Find Full-Text Articles
Save Search Results
Saving Searches & Creating Alerts
My NCBI Accounts
Literature Reviews

Finding a few good good articles on a particular topic can be very easy.

State your question as specifically as possible:

Is acupuncture effective for the treatment of migraine headaches?

Identify the key words in your question:

acupuncture

Type the key words into the search box:

acupuncture AND migraine

PubMed searches for the keywords in the article title, abstract and subject headings. It does not search the full text of the article.

Results show up with the most relevant articles, as predicted by PubMed, first in summary format. Click on the title for more information about a single article, change your search by adding, editing, or deleting terms in the search box, or change the Display Settings to view the most recent articles first.

PubMed results page after performing a search for "accupuncture AND migraine"

Go to a PubMed search for: acupuncture AND migraine

Enter the author's name in the following format: Author's Last Name Initials. Example: Corbie-Smith G

An author search for Corbie-Smith G in PubMed

No periods or commas are necessary.
Adding the second initial can help focus the search on a particular person, but may cause you to miss articles published by the author that do not include the middle name.
Searching by the authors full first name will work sometimes but not always, so it's better to use their initial(s).

Go to this author search in PubMed: Corbie-Smith G Notice that the last (oldest) article retrieved was published in May 1997.

You can also search for a specific journal in PubMed and set up an alert to see new articles in your favorite journals.

If that does not work, you can try searching for the journal by title on the main search bar without the journal field. We don't recommend that as a first strategy because you might get results from several journals with similar names.

If you see results from your target journal, you can select one and click on the journal abbreviation to search for the journal by its abbreviation.

Contact a librarian if you would like support with your search strategy.

To find a specific article when you know some of the publication information such as journal name, publication date, page numbers, author name, or title words, use the Single Citation Matcher form.

The link to Single Citation Matcher is on the PubMed homepage. It is the third item in the second-left section labeled Find, below the search box.

You only need to fill in a few data points. Author Name and First Page often bring up a single result.

Go to the PubMed Single Citation Matcher

To run a more advanced search in PubMed or see your search history, select Advanced below the search box.

The Advanced Search link is located under the PubMed search box

On the PubMed Advanced Search page:

You can add specific search terms using the Add Terms to Query Box option.
To limit your search to articles published in one journal, select Journal from the drop-down box.
Start typing the journal name in the Search box.
Select the correct full journal name from the auto-complete list.
With the correct full journal name inserted in the top search box, click Add and the journal will be added to your search. You can then click the search button or then add more terms to your search.
To limit your search to English, select Language from the drop-down box, type "English", then click add.
You can also see your Search History of the recent searches you've run, see the Details of how PubMed interpreted your search terms and Add your previous search back to the Search Design box to be edited or added to.

Go to the PubMed Advanced Search page .

A truncation search feature provides the ability to search for variant words or spellings.

To search for all terms that begin with a word, enter the word followed by an asterisk (*): the wildcard character.

To search for a phrase including a truncated term, use the following formats:

Enclose the phrase in double quotes: "breast feed*"
Use a search tag: breast feed*[tiab]
Use a hyphen: breast-feed*

At least four characters must be provided in the truncated term.

The truncated term must be the last word in the phrase.

Using the PubMed truncation feature also has some specific consequences:

Automatic Term Mapping (ATM) is turned off;
The truncation function looks for only the first 600 variations, so the search could be incomplete;
Truncation can cause a search to time out if an excessive number of variants are generated.
Truncation can cause lengthy and confusing error messages for My NCBI updates

For example, heart attack* will not map to the MeSH term Myocardial Infarction or include any of the more specific terms, e.g., Myocardial Stunning; Shock, Cardiogenic.

See the PubMed User Guide for more examples and information about advanced search features in PubMed.

How to Build a Proximity Search in PubMed

To create a proximity search in PubMed, enter terms using the following format:

"search terms"[field:~N]

Search terms = Two or more words enclosed in double quotes.
Field = The search field tag for the [Title] or [Title/Abstract] fields.
N = The maximum number of words that may appear between your search terms.

For example, to search PubMed for citations where the terms "hip" and "pain" appear with no more than two words between them in the Title search field, or in the Title/Abstract search field, try the search:

"hip pain"[Title:~2] "hip pain"[Title/Abstract:~2]

Search results may include hip pain, hip-related pain, hip joint pain, hip/groin pain, hip biomechanics and pain, pain after total hip arthroplasty, pain in right hip, and more.

See the PubMed User Guide and view the proximity searching tutorial for more examples and information about proximity searching in PubMed.

Proximity Search Now Available in PubMed. NLM Tech Bull. 2022 Nov-Dec;(449):e4.

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Next: Filters and Narrowing Searches >>
Last Updated: Dec 19, 2023 3:00 PM
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Pubmed: searching tips & more: home, accessing pubmed.

Direct link to this guide: guides.lib.berkeley.edu/pubmed .

This PubMed exercise set (docx) will help get you started using PubMed's search features, including filters, field tags, MeSH, and more.

The PubMed FAQ and User Guide is your best bet for up-to-date PubMed help.

PubMed Searching: Top Tips; Details Below

Combine terms with AND or OR ;
Use Filters (eg, Ages, Article types, Languages, etc.);
Search for your term as a word in the title or title or abstract (using Filters, Advanced Search, or Field Tags );
Use MeSH (Medical Subject Headings), with subheadings ;
Use the Similar articles link located by each citation;
Always keep in mind the question you are trying to answer when creating a search strategy and when reviewing the articles you find .

Search Tips

Looking for a known article? In the Search box, simply type the article title, or a combination of article title words with author and/or journal name words, and click Search. PubMed's citation sensor will automatically analyze your query for citation information to return the correct citation. More information in the PubMed User Guide .

Subject Searching : For most literature reviews, it may be best to search narrowly: think of your topic in specific concepts: Who is your population ? Are you looking at a specific outcome or intervention ? Are you interested in a specific geography ? Do you only want studies that used a particular method ? Build your search using the concepts that describe what you want, then broaden or narrow the search as needed. Consult our Searching the Public Health Literature More Effectively Guide for more literature searching tips.

Search specific fields (author name or affiliation, word in title and/or abstract, journal name, etc.) : Two methods: Field Tags or Advanced Search

Field tags exist for every field in a PubMed record. For example, type cell[ta] to search for articles in the journal Cell. Affiliation (tag is [ad]) can include any field in an author's address. Type cell[ta] AND (berkeley[ad] OR 94720[ad]) to find articles in Cell by Berkeley authors. A complete list of field tags (there are several dozen) is in the PubMed User Guide .

Click Advanced under the search box to be taken to PubMed's Advanced Search page where you can use a drop-down menu to specify fields to be searched. Click Add to add the term to the search box. You may then use the drop-down menu to add another field to be searched; click AND (or change to OR or NOT), to add it to the Query box:

pubmed advanced search builder with affiliation search indicated

More information in the PubMed User Guide

Looking for a Plain Language Summary? In the Search box, type AND hasplainlanguagesummary after your search terms. Each citation your search finds will have a plain English summary of the article. Example: asthma inhalers AND hasplainlanguagesummary .

New! Proximity Searching: Search for multiple terms appearing in any order within a specified distance of one another in the [Title] or [Title/Abstract] fields. To create a proximity search in PubMed, enter terms using the following format:

"search terms"[field:~N]

Search terms = Two or more words enclosed in double quotes.
Field = The search field tag for the [Title] or [Title/Abstract] fields.
N = The maximum number of words that may appear between your search terms.

For example, to search PubMed for citations where the terms "hip" and "pain" appear with no more than two words between them in the Title/Abstract search field, try the search:

"hip pain"[Title/Abstract:~2]

Search results may include hip pain, hip-related pain, hip joint pain, hip/groin pain, hip biomechanics and pain, pain after total hip arthroplasty, pain in right hip, and more.

See the PubMed User Guide and view the proximity searching tutorial for more examples and information about proximity searching in PubMed.

Search History

Click Advanced (under the Search box) and scroll down to see your search history . Click > in the Details column to see how PubMed translated your query. Click the number in the Results column to go back to the search results for that search. You can build new searches or revise past searches here; details in the PubMed User Guide . It is highly recommended that you download your search history by clicking the Download button. This will help you keep track of what and when you searched; this is especially important when doing a systematic review.

Exporting Citation to Reference Management Software

EndNote: Open your EndNote library. Select the PubMed citations to export by clicking in the checkbox to the left of each. Click Send to (just under the search box) and choose Citation manager , then click Create file . The first time you do this, make sure EndNote or Research Soft Direct Export is selected as the Open with program; navigate to the EndNote program on your computer if necessary. Click OK.

RefWorks (Note: UC Berkeley's access to RefWorks will end in 2024): Tools . Click Install Save to RefWorks , then drag the bookmarklet to your favorites toolbar.--> Note: Currently, RefWorks Save to RefWorks bookmarklet does not always work with PubMed. In PubMed, select the citations to export by clicking in the checkbox to the left of each. Click Save , and make sure Selection (number) is what you see on that drop-down menu. Choose PubMed in the Format menu, and then Create file . Save the file to somewhere you will remember. In RefWorks, select Add > Import references . Select a file from your computer or drag and drop it onto the import page, then click Import . You can select which RefWorks folder to import into.

Zotero: Note: Currently the Zotero Connector does not always work with PubMed. In PubMed, select the citations to export by clicking in the checkbox to the left of each. Click Save , and make sure Selection (number) is what you see on that drop-down menu. Choose PubMed in the Format menu, and then Create file . Save the file to somewhere you will remember. In Zotero, click File > Import... > A File, and navigate to the file you saved. Select it and click Open.

Mendeley: In PubMed, select the citations to export by clicking in the checkbox to the left of each. Click Save , and make sure Selection (number) is what you see on that drop-down menu. Choose PubMed in the Format menu, and then Create file . Save the file to somewhere you will remember. In Mendeley, select the folder you want to import into, and click Add Files . Select the search results file.

Others: Most citation managers will let you import a text file in RIS format . Save citations in the PubMed format , which is comparable to RIS format, then import into your citation manager.

More information in the PubMed User Guide .

PubMed's "Cite" feature: Another option: When you are viewing a record, click Cite on the right side, select the desired citation style, then click Copy . You may also click Download .nbib to download and add that single citation to your reference management software.

Using Search Filters

After running a search, use the filters on the left side to limit your search results. Click Additional filters to add filtering options such as article type, language, and age groups. Note that selected filters will "stick" for future searches until you de-select them. More information is in the PubMed User Guide .

Important note : Using filters will have the effect of limiting your search results to include only citations with MeSH terms applied; see below on what will be excluded by limiting your search to only include citations with MeSH terms.

Using MeSH (Medical Subject Headings)

Why MeSH? Using Medical Subject Headings, or MeSH , may help you retrieve more relevant search results. MeSH are the subject terms applied to nearly all PubMed citations. However, it is important to remember that some PubMed citations - including the very newest citations - do not have MeSH terms applied to them, and therefore will not appear in a search that exclusively uses MeSH terms.

Three ways to search using MeSH :

Use the MeSH terms from a known, relevant article : Search for a known article, click to open the full record, then scroll down to see the MeSH terms applied to that article. Clicking on a MeSH term will allow you to either search PubMed using only that term, or add that term to the search box; you can then add additional terms and execute your search.
Use the MeSH Database to find terms, then build a search using those terms : Step-by step instructions may be found in the PubMed User Guide (scroll down to "Launch PubMed searches from the MeSH database").
Use Advanced Search to search using known MeSH terms : If you know the MeSH terms you want to use for your search, click Advanced under the Search box, then use the drop-down menu in Advanced Search. Instructions in the PubMed User Guide .

To see suggested MeSH terms based on a block of submitted text (ie, an abstract, article, etc.), use the MeSH on Demand tool . MeSH on Demand also lists similar PubMed articles relevant to your submitted text, thus MeSH on Demand can help you find articles similar to a known, relevant article.

MeSH Subheadings (or " Qualifiers ") help focus your search results more precisely. In the MeSH Database, select desired subheadings, then click Add to search builder , then click Search PubMed :

MeSH Database showing subheadings of a MeSH term

Help & Tutorials

The PubMed User Guide is updated frequently. It includes FAQs on most common search issues, as well as search tips and more. Examples:

I retrieved too many citations. How can I focus my search?
I retrieved too few citations. How can I expand my search?
How do I find systematic reviews ?

PubMed's Online Training website includes numerous tutorials, guides, and handouts.

See also the UC Berkeley Library PubMed Quick Guide for more tips and examples.

Alternative PubMed Search Interfaces

The Medline subset of PubMed , which consists of articles assigned MeSH terms, and comprises the overwhelming majority of PubMed citations, is available for searching in both Ovid Medline and in Embase .

PubMed PubReMiner lets you enter a search, and the results will list terms in a frequency order : you will see lists of MeSH terms, title words, abstract words, authors, journals, etc., in the frequency order each of these appears in your search result. This can help you come up with additional terms to include in your search, as well as the top publishing authors and journals on your topic.

Use PubMed by Year to see a graph the number and rate per 100,000 citations of your search terms, and to compare to the frequency of various terms over time. PubMed by Year searches from the oldest citations in PubMed (1781) to the current year. Data downloadable to csv or svg.

PubVenn enables you to explore PubMed using Venn diagrams. Enter any multi-term search to see the relative size of the citation set for each term as well as how those sets interact. The resulting diagrams are printable.

Once you have a PubMed search strategy, you can use the Polyglot Search tool to translate your PubMed search into the correct syntax for several other databases, including Embase, Scopus, Web of Science, and more.

My NCBI: Customizing PubMed & More

My NCBI allows you to save searches and citations, customize PubMed, and more. Click Log in (top right of PubMed home page); you will be presented with several options. Unless you already use an eRA or NIH logon, we strongly suggest you select more login options , then start typing berkeley and choose University of California, Berkeley . You can then log in using CalNet. Once logged in, click your user name (top right) then select Dashboard (My NCBI) . Here will be your:

Saved Searches (which you can have re-run as an "alert" on a schedule);
Collections (citations you have saved, which you can share if desired);
Custom filters you have created;

and more. Detailed instructions may be found in My NCBI Help . If you already are a NCBI user, please note: As of June 2022, you will be required to login using a 3rd-party option .

Last Updated: Apr 11, 2024 1:26 PM
URL: https://guides.lib.berkeley.edu/pubmed

Expert Searching
Literature Searching Services
Literature Review Process
Formulating Your Research Question
Which Databases to Use
Choosing Search Terms
Combining Search Terms
Finding Spelling Variations
Search by Parts of a Citation
Limiting a Search with Filters
Saving Your Search
Finding Related Articles
Systematic Reviews
Searching with Google
Other Types of Reviews
PubMed Search Tips

1. When to Use PubMed

2. pubmed cool tools, 3. how to use mesh: medical subject headings, 4. how to use keywords, 5. pubmed pro tips, 6. combining search terms and concepts, 7. saving your searches, 8. pdf printable handout, 9. pubmed practice, 10. more information.

Embase Search Tips
Cochrane Search Tips
More Welch Guides

New PubMed Video

Accessing Full Text

Find it @ jh.

In most databases, the FIND IT icon will often appear within an item record. Clicking the FIND IT icon will take you to a catalog page showing a list of full-text options.

You can also search FIND IT directly.

Interlibrary Loan

If the full-text is not available, you will see a heading that says, "Request a copy from Interlibrary Loan." Click on "Welch Medical Library Borrowers" link to request the article free of charge (available for Hopkins affiliates).

You can also submit an Interlibrary Loan request manually.

New PubMed FAQs and User Guide
New PubMed: Highlights for Information Professionals
New PubMed: Trainer's Toolkit
When to Use PubMed
PubMed Cool Tools
How to Use Medical Subject Headings
How to Use Keywords
PubMed Pro Tips
How to Combine Search Terms and Concepts
How to Save Your Searches
Printable PDF Handout
PubMed Practice
More Information
Access PubMed (Hopkins affiliates)
Access PubMed (non-Hopkins affiliates)

PubMed is a platform that indexes journal articles and more back to 1947. It covers the areas of medicine, nursing, dentistry, veterinary medicine, health care systems, preclinical sciences, and related areas. PubMed is developed and maintained by the National Library of Medicine (NLM) and the National Center for Biotechnology Information (NCBI), both at the National Institutes of Health (NIH) in Bethesda, MD. As of December 2020, PubMed contains over 30 million records. PubMed is a platform that contains MEDLINE, PubMed Central, and additional PubMed records.

Clinical Queries

Use Clinical Queries to enable pre-made filters applying to different clinical research areas. Results are delineated into clinical studies, systematic reviews, and medical genetics. Enter your search exactly as you would in the PubMed search box.

For more information, see:

Documentation on the Clinical Studies filters
Documentation on the Medical Genetics filters
Documentation on the COVID-19 filters

Journals in NCBI Databases

Use Journals in NCBI Databases to limit your search to a specific journal or to find out more information about journals indexed in MEDLINE. text

Single Citation Matcher

Use the Single Citation Matcher to find citations in PubMed. You may enter or omit any field.

Special Queries

Searchers at the National Library of Medicine have created search filters for multiple common topics. See and enable them on the PubMed Special Queries page .

Controlled Vocabulary: MeSH

MeSH Database
NLM's MeSH Tutorial
MeSH stands for Medical Subject Headings . It is a controlled vocabulary of terms assigned to records to make them discoverable.
These are a standardized set of terms that are used to bring consistency to the searching process. In total, there are approximately 29,000 MeSH terms , and they are updated annually to reflect changes in terminology.
Use the MeSH database to identify Medical Subject Headings (MeSH) which will help you to find literature indexed with the MeSH term.
Using MeSH terms helps account for variations in language, acronyms, and British vs. American English.
MeSH can be searched from the MeSH Database
Terms are arranged hierarchically by subject categories with more specific terms arranged beneath broader terms. MeSH terms in PubMed automatically include the more specific MeSH terms in a search. This is called "explode."
To turn off this automatic explode feature, click on the button next to, "Do not include MeSH terms found below this term in the MeSH hierarchy" in the MeSH record or type [mesh:noexp] next to the search term, e.g. neoplasms [mesh:noexp] . See next page for additional information on no explode.
Use the PubMed Search Builder on the right side of the screen to add your selected MeSH term to the box, and click Search.

Explode, No Explode, and Major Heading

Explode will search with all narrower headings beneath the main heading you have chosen. PubMed will default to explode any MeSH term you search.
No Explode will only search for your chosen MeSH term without including any of the narrower headings in the MeSH hierarchy. You can select this option from the MeSH record.
Major Heading will narrow your search to only find MeSH terms listed as a major topic of an article. You can select this option from the MeSH record. Major headings are shown in the article record with an asterisk.

Subheadings

MeSH terms can be made more specific by the addition of correlated or free-floating subheadings .

When in the MeSH record, add subheadings by clicking on the boxes next to the desired subheadings. Then click "Add to Search Builder." Warning: Adding too many subheadings may lead to missing important articles.
MeSH/Subheading Combinations: You can manually add subheadings in the search box by using the format MeSH Term/Subheading, e.g. neoplasms/diet therapy . See abbreviations of MeSH subheadings, which can also be used. ( MeSH Subheadings ).
For a MeSH/Subheading combination, only one subheading at a time may be directly attached to a MeSH term. For example, a search of Hypertension with the subheadings Diagnosis or Drug Therapy will appear as Hypertension/diagnosis [mesh] OR Hypertension/drug therapy[mesh] .
As with MeSH terms, PubMed search results, by default, include the more specific terms arranged beneath broader terms for the MeSH term and also includes the more specific terms arranged beneath broader subheadings .

Automatic Term Mapping

PubMed uses Automatic Term Mapping (ATM) when you enter terms in the search box.
Automatic Term Mapping means that the search terms you type into the search box are automatically mapped to MeSH terms.
To see Automatic Term Mapping in action, click on the Details arrow in your Advanced > History and Search Details box.
Using quotes around a phrase or truncation turns off Automatic Term Mapping. The terms are instead searched exactly as entered, with no mapping applied.

Using Keywords

Keywords can be any words used to describe your idea or concept.
Keywords can be single words or phrases.
Use quotes around all phrases to ensure that the phrase is searched together.
For more ideas, visit the MeSH database and look at the entry terms listed in the MeSH record.
Also consider using synonyms, acroynyms, initialisms, variations in spelling, and other closely-related terms used interchangeably to describe the topic.

Keyword Generation

Keywords can be generated by:

browsing entry terms in PubMed's MeSH, and synonyms in Embase's Emtree to add additional keywords to a concept;
looking at a few key articles and seeing how the terminology is used; and by
doing a few preliminary searches and browsing the results to see how the terminology is used.

You can use filters to narrow your search results by article type, text availability, publication date, species, language, sex, subject, journal category, and age. See more on Filters on PubMed's Help guide .

On the left side of the results are options to filter your search.
You can access additional filters through the link at the bottom of the filters bar.
"Text availability" is for users who are not affiliated with an institution. You do not need to limit by text availability since you have access to the JH Catalog and Interlibrary Loan.
Use filters cautiously. Limits other than date or language will limit your search to MeSH-indexed records only.
For example, if you would like to limit your results to "human studies," use the following search to exclude animal studies instead of using the "humans" limit from the search results page. Simply add this to the end of your search:

Phrase Searching

Surround phrases with double quotes to search as a phrase to use a more specific search with more precision, and not as disparate words, which will result in a more sensitive search with higher recall. See more on Phrase Searching on PubMed's Help guide.

In PubMed you can use a * at the root of a word to find multiple endings. For example:

Note: In New PubMed, you can now truncate a phrase inside quotes. "catheter infection*" will return catheter infections. See more on on Truncation on PubMed's Help guide .

Use search field tags to specify in which field the database queries for the search term. In PubMed, first type the search term and then the search field tag in brackets. e.g. Cardiology [tiab] searches for cardiology in the title and abstract.

[All Fields] or [all] – Untagged terms and terms tagged with [all fields] are processed using Automatic Term Mapping . Terms enclosed in double quotes or truncated will be searched in all fields and not processed using automatic term mapping.
[Text Words] or [tw] – Includes all words and numbers in the title, abstract, other abstract, MeSH terms, MeSH Subheadings, Publication Types, Substance Names, Personal Name as Subject, Corporate Author, Secondary Source, Comment/Correction Notes, and Other Terms.
[Title/Abstract] or [tiab] – Words and numbers included in the title, collection title, abstract, and other abstract of a citation. English language abstracts are taken directly from the published article. If an article does not have a published abstract, NLM does not create one.
NCBI explanation of Field Descriptions and Tags

Boolean Operators

A comprehensive search of PubMed will include both controlled vocabulary (MeSH) and keyword terms.
Boolean operators are used to combine search terms. In PubMed, you can use the operators AND, OR, and NOT.
Go to the “Advanced Search” page to combine searches. This is where your search history is located during your search session.
Boolean operators MUST be used as upper case (AND, OR, NOT).
OR —use OR between similar keywords, like synonyms, acronyms, and variations in spelling within the same idea or concept
AND —use AND to link ideas and concepts where you want to see both ideas or concepts in your search results
NOT —use to exclude specific keywords from the search; however, you will want to use NOT with caution because you may end up missing something important

To save searches and create alerts in PubMed, you must first create an account.

Create an NCBI account* by clicking on the "log in" button in the upper-right corner of the screen (sign up for a My NCBI account ).
Once you complete a search, click on Create alert underneath the search box. From here you can create a search alert or save your search strategy.

*NLM changed the way you need to log into NCBI in 2021. Information and updates can be found in NCBI Insights.

PubMed Searching Tips
PubMed Tips 2020

If you would like to practice comprehensive searching in PubMed, use the links below to access PubMed, and the three worksheets to achieve steps within the search process. See also the National Library of Medicine's Training Module on Using PubMed in Evidence-Based Practice .

More detailed information about MeSH vocabulary
Use of MeSH in Online Retrieval
PubMedTutorials
PubMed YouTube Channel
Open Educational Resource: PubMed in Evidence-Based Practice

Related Research Guides

Systematic Reviews and Other Expert Reviews
Citation Management
<< Previous: Database Tipsheets
Next: Embase Search Tips >>
Last Updated: Apr 4, 2024 9:04 PM
URL: https://browse.welch.jhmi.edu/searching

PubMed: Run a Search

Run a Search
Find Full Text
Refine Searches
Find Research Articles
MeSH/Advanced Search
Export to Citation Managers
New PubMed Essentials

Step-by-Step Instructions

Step 1: enter your terms.

Type any key word or phrase into the Search box. Use an asterisk (*) to retrieve variations on a word, e.g., bacter* retrieves bacteria, bacterium, bacteriophage , etc.

For a Subject Search: Enter one or more words (e.g., asthma drug therapy ) in the Search box and click on Search. PubMed automatically "ANDs" (combines) terms together so that all terms or concepts are present, and it translates your words into MeSH (Medical Subject Headings) terms.
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Research Highlight
Published: 12 April 2024

Peripheral cancer remodeling of central neural system

Gray Umbach 1 &
Shawn L. Hervey-Jumper ORCID: orcid.org/0000-0003-4699-260X 1

Cell Research ( 2024 ) Cite this article

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Recent work in the field of cancer neuroscience has demonstrated bidirectional interactions between neurons and cancer cells ultimately influencing neural circuit function, tumor growth, and patient survival. In a recent paper published in Cell Research , Xu et al. take a novel approach, identifying a secretory factor-induced central nervous system-mediated increase in peripheral autonomic sympathetic activity that drives tumor–immune cell interactions and tumor progression across multiple cancer types.

Since 1938, with the identification of the histopathological colocalization of neurons and brain tumor cells, 1 biologists have suspected that the central nervous system (CNS) participates in CNS tumor biology. Confirming this suspicion, recent studies have delineated a multitude of mechanisms by which this interaction occurs, including via paracrine signaling, 2 systemic and immune cell-mediated interactions, 3 and even direct synaptic connections between neurons and both glial and metastatic neoplastic cells within the CNS. 4 , 5 At the circuit level, specialized neurons engage in neuronal computations following infiltration by low- and high-grade gliomas. 6 However, CNS circuit remodeling mediated by peripheral cancer without evidence of brain metastasis has many unanswered questions. Xu et al. 7 innovatively apply a top-down approach, using multi-omics across four peripheral cancers to identify secretory factors convergent on a similar neuronal pattern within the CNS (Fig. 1 ).

Tumor cells from multiple organs (including breast in pink, lung in blue, colon in green, and prostate in grey) influence the CNS through tumor-derived cytokines with similar features across cancers. Reciprocally, CNS structures within the hypothalamus regulate systemic sympathetic tone. Figure generated with BioRender.com.

First, they observed that peripheral xenografts of murine breast, lung, prostate, and colon cancers not only demonstrated neuronal activity within central brainstem structures, but many activity patterns overlapped across cancer types. Activated CNS structures included cranial nerve nuclei and the paraventricular nucleus of the hypothalamus (PVN), a region responsible for sympathetic output. Next, hypothesizing that this common activity profile could stem from shared secretory factors across cancers, they conducted parallel transcriptomic and proteomic analyses revealing 61 candidate factors expressed by all cancers, of which two, leukemia inhibitory factor (LIF) and galactin-3 (Gal3) were sufficient for inducing the observed brain activity phenotype following intraperitoneal administration.

Xu et al. further demonstrated the necessity of LIF and Gal3 for inducing PVN neuronal activity by generating knockout mice. While knocking out LIF blunted but did not entirely abrogate the conserved activity profile, Gal3 knockout prevented activity in all identified regions. They were able to achieve similar network quiescence by the administration of LIF and Gal3 small-molecule inhibitors to tumor xenografted mice thereby offering overlapping models for the ablation of their target tumor cell-derived cytokines. In both the inhibitor and knockout models, in addition to a rescued brain activity state, mice experienced delayed tumor growth suggesting a role for CNS sympathetic inputs for peripheral malignancy growth.

Following the demonstration that these secreted factors both lead to a convergent brain activity profile and are associated with tumor progression, they hypothesized that this increased rate of tumor growth may stem from a neuroimmune mechanism. Indeed, they uncovered lower counts of myeloid-derived suppressor cells (MDSCs) in the blood and tumor tissue of their knockout mice. Removal of sympathetic inputs from peripheral tissues of tumor model mice also lead to lower MDSC counts. However, doing the same in the knockout mice did not drop MDSC counts further, revealing no additive effect of sympathetic activity with secreting factors LIF and Gal3. The authors posit that this finding suggests that both factors take their action via sympathetic signaling.

Taken together, the authors demonstrate across four peripheral cancer models that paracrine signaling factors Gal3 and LIF activate the PVN, leading to increased sympathetic input into tumor cells and peripheral tissues responsible for MDSC generation, promoting tumor proliferation. Importantly, this discovery is uniquely generalizable across four distinct malignancies thereby representing a common pathway by which solid organ cancers may hijack neuronal systems to drive their own growth.

Notably, sympathetic nerve activity has previously been shown to influence peripheral tumor growth, invasion, and metastatic spread in several tumor types. 8 , 9 , 10 Further, the discovery that the CNS may remotely influence peripheral tumors via the autonomic or peripheral nervous system has previously been demonstrated. 3 However, Xu et al. approach cancer regulation of neuronal systems in the brain from a fresh perspective, searching for conserved strategies that peripheral tumors use to harness the CNS to promote invasion. In doing so, they effectively demonstrate the far-reaching role that the brain plays in cancer found within and beyond itself and raise the possibility of more broadly applicable cancer neuroscience-based therapeutic strategies.

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Published: 15 April 2024

Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research

Safoora Masoumi ORCID: orcid.org/0000-0002-7343-1771 1 ,
Hossein Amirkhani 2 ,
Najmeh Sadeghian 3 &
Saeid Shahraz 4

Systematic Reviews volume 13 , Article number: 107 ( 2024 ) Cite this article

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Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model.

Scanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy.

The trained models’ micro f1-score for classifying abstracts, into three variables were 77.35% for COU, 76.24% for TS, and 85.64% for PRF.

The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95% CI : 56.80–78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions ( AAGR = 39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%).

Conclusions

BioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews.

Peer Review reports

The history of natural language processing (NLP) is relatively short, but it has seen rapid growth through multiple fundamental revolutions. Alan Turing invented a test in the 1950s to determine whether computers could think like humans [ 1 ]. NLP scientists then applied universal linguistic rules to textual data to understand it. During this time, Noam Chomsky’s universal theory of language dominated NLP scientists’ attention. Computer scientists replaced this linguistic approach with computational models based on statistical analysis [ 1 ]. Increasing computational power for analyzing a large amount of textual information has contributed to our current understanding of NLP and its applications due to the invention of machine learning methods, especially deep learning [ 1 , 2 , 3 ]. Our intelligent machines now need natural language processing (NLP) to decipher meanings from human languages. With the widespread availability of smart gadgets in everyone’s life, NLP has become even more advanced over the past two decades [ 4 , 5 ]. Machines cannot recognize phrases and expressions without NLP in spoken and written languages. Moreover, the enormous amount of unstructured data produced daily highlights the need for NLP to assist professionals in sorting out their information [ 2 , 4 ]. Evidence-based medicine relies on systematic literature reviews to answer specific questions from a large amount of textual data, which can be challenging and time-consuming [ 6 ].

Machine learning and natural language processing can speed up and improve the SLR. In this context, text classification and data extraction are two NLP-based strategies. Abstract screening is an essential application of text classification in literature reviews. Alternatively, data extraction identifies information about a particular variable of interest. NLP can, for example, help extract the number of individuals who participated in particular clinical trials [ 6 , 7 ]. BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based machine learning model for language modeling that has demonstrated significant success in various NLP tasks [ 8 ]. BioBERT, a BERT-based model pre-trained on biomedical texts, has outperformed other pre-trained language models in some biomedical datasets [ 9 ]. BioBERT has been highly performant in previous studies [ 10 , 11 , 12 , 13 ].

In this study, we deliberately analyze the evolution of medical NLP over the last two decades and benchmarked some of our findings against two similar studies published recently [ 14 , 15 ]. As an example of how NLP aids abstract review, we conducted an SLR using an automated method. Based on the results of SLR, a list of data sources used in medical NLP literature is provided, along with the type of NLP application and the related disease areas. We also show how the BioBERT model categorizes abstracts.

Developing training data

PubMed, Embase, PsycINFO, and CINAHL were searched using controlled vocabulary thesaurus (MesH in PubMed and Emtree in Embase) and free-text keywords. The search queries included “natural language processing” and “text mining.” Additional file 1 provides the full search queries. Also excluded were editorials, case reports, commentary, erratum, replies, and studies without abstracts. Before 2000, there were few NLP studies. Therefore, we included all abstracts published between January 2000 and December 2020. Multiple steps are involved in the study. First, we classified NLP abstracts based on their text source (e.g., social media versus clinical notes). After optimizing retrievable meaningful classes of abstracts, a finalized training dataset was created. Next, we calculated the classification accuracy of the computer algorithm using the entire corpus. As a final step, we applied the algorithm to obtain the classes and visualized them. The last author (S. S.) randomly selected 100 abstracts from PubMed and classified their text sources, the context of use (e.g., abstracts pertaining to clinical decision support vs. those related to NLP method development), and the type of medical conditions studied. Using these primary classes, the lead author (S. M.) and third author (N. S.) explored more classes and categories for each of these classes in further PubMed abstracts. By adding more abstracts, they continued to find more classes and subgroups until they were unable to find any more classes and subgroups. The saturation process was completed after reviewing 485 abstracts. All authors discussed and optimized the classification iteratively until they reached an agreement on the final classification. In Table 1 , the finalized classes and their definitions are described.

As depicted in Fig. 1 , machine learning algorithms were used to classify abstracts in the final corpus into those obtained from the trained dataset. By fine-tuning the pre-trained language models ubiquitous in modern NLP, we followed the favored approach. BERT, or Bidirectional Encoder Representations from Transformers, is a language model developed by Google [ 8 ]. The models are pre-trained on large corpora and then fine-tuned using task-specific training data by reusing the parameters from the pre-trained models. We used the BioBERT model [ 9 ] from the Hugging Face Transformers library [ 16 ], which was trained on abstracts from PubMed and full articles from PubMed Central. Then we fine-tuned three different models, one for each of our targets: text source, context of use, and primary research fields. The hyper-parameters, such as the learning rate and number of epochs, were selected using cross-validation. The final model was trained on the entire training data using the optimized hyperparameters. Since we utilized a pre-trained BioBERT model, a standard GPU, such as the Nvidia Tesla K80, was sufficient for fine-tuning the model during both the training and inference phases. All the experiments were conducted in a Google Colab environment leveraging this GPU.

Overview of the proposed machine learning approach

For each target variable, we fine-tuned three different classifiers as an alternative method of improving the models’ accuracy. Repeating the fine-tuning process resulted in a different classifier due to different data batches used during training. The final prediction for an input article was obtained by majority voting of the base classifiers’ predictions. Afterwards, the trained models were applied to the entire corpus. A set of 362 randomly selected abstracts was manually annotated by the lead (S. M.), third (N. S.), and last author (S. S.) to evaluate the labels provided by the trained models. Next, the human annotations were compared to those provided by the models. The evaluation showed that the trained models’ accuracy in classifying abstracts into the text source, the context of use, and the primary research field was sufficient, mainly to track the time trends of the classes. Therefore, we assumed that misclassifications would remain constant over time. Our next step was to fit models that indicated publication growth rates for different study subgroups using ordinary least-squares regression. Citations were the dependent variable, and publication year was the predictor. Per year, the coefficient of the predictor showed an average increase in citations. A squared term for the publication year was added to the primary model to determine if the growth was linear or exponential. The increase in R 2 indicated logarithmic growth. The average annual growth rate (AAGR) was calculated by averaging all annual growth rates (AGR) over the study period (sum of AGRs/number of periods). We calculated AGR as the difference between the current year’s value and the past year’s value divided by the past year’s value.

We report a micro f1-score to evaluate the trained models. The f1-score is calculated as the harmonic mean of precision and recall for the positive class in a binary classification problem.

True positive (TP) and true negative (TN) are the numbers of samples correctly assigned to the positive and negative classes, respectively. On the other hand, false positive (FP) and false negative (FN) are the numbers of samples that are wrongly assigned to the positive and negative classes, respectively. Accuracy is the ratio of the samples correctly assigned to their respective classes.

Precision (P) and recall (R) are calculated as follows if TP, FP, and FN represent the number of true-positive, false-positive, and false-negative instances, respectively:

And f1-score will be as follows.

The average of the f1-scores obtained for different classes is computed for multiclass problems, such as ours. We report the weighted average considering the number of instances in each class in order to account for label imbalance.

Based on the evaluation, the trained models classified abstracts accurately into their text source, context, and primary research field (disease area) by 78.5%, 77.3%, and 87.6%, respectively. Accordingly, the trained models’ micro f1-scores for classifying abstracts into their text source, context of use, and primary research field were 77.35%, 76.24%, and 85.64%, respectively. We retrieved 22,294 English abstracts from the database. There were 12,817 references left after removing 8815 duplicates, 500 articles without abstracts, 32 errata, 31 commentaries, 31 editorials, and 68 veterinary-related abstracts. The selected analyses were based on 12,161 abstracts, excluding those published in 2021. Figure 2 illustrates the abstract selection process for creating the final abstract collection. NLP publications have increased logarithmically since 2000, as shown in Fig. 3 .

PRISMA flowchart illustrating the steps of abstract selection for building the final corpus. *For most analyses, we excluded abstracts for the year 2021, leaving 12,161 abstracts in the analysis data

Trend analysis of 12,817 abstracts showing the overall trend of the growth and the number of articles per year

The Additional file 2 conveys the total number of abstracts retrieved for each subgroup. Table 2 shows the AAGR and average growth slope (coefficient) with a 95% confidence interval. It also displays the adjusted R2 of the regression model with and without a squared term for the publication year. The AAGR was 20.99%, with an average increase of 72 (95% CI : 56.80–78.30) publications per year. According to the adjusted R2 of 83%, the publication number is strongly affected by time. After adding a squared term for publication year, the indicator increased to 93%, indicating logarithmic growth. In all types of NLP text sources, electronic medical or health records or similar electronic clinical notes accounted for the highest percentage (57.12%). The addition of published articles and other sources of medical evidence accounted for 33.84% of all NLP text sources. Social media, including websites and databases with omics data (e.g., genomics), accounted for less than 10% of all NLP text sources (Table 2 ). Figure 4 displays the relative proportions and growth trends of four specific subgroups of text sources since the year 2000. Additionally, it presents the percentage representation of these chosen subgroups within the total for the “context of use” of the text sources. Despite comprising only 4.91% of publications, the so-called omics text data exhibited the fastest growth ( AAGR = 65.08%) among all other text sources.

Proportion and growth of four selected subgroups of text source since the year 2000 and percentage of selected subgroups of the “context of use” of the total for the subgroups of text source

Changes in the dominant primary research fields since 2000, along with the expansion rates, as well as the distribution percentages for specific subcategories within “context of use” and “text source,” are illustrated in Fig. 5 . Four medical fields accounted for slightly over 65% of all the research NLP researchers conducted and published (neoplasms, mental conditions, infectious diseases, and circulatory diseases). Neoplasms topped this list. The growth rates of all these medical fields were comparable (Table 2 and Fig. 5 ). NLP methods for clinical decision support were the most notable identifiable application among different aims (called “context of use”) of NLP studies, accounting for 25.45% of all publications. In contrast, bioinformatics-related discoveries showed the highest growth ( AAGR = 69.65%) among all medical NLP applications, in line with the highest growth of omics databases. Among the subgroups under “context of use,” the majority belonged to “other medical fields,” which included a wide range of medical applications. Changes in the “context of use” since the year 2000, including its proportion and growth, along with the percentage representation of specific subgroups within the “text source,” are depicted in Fig. 6 (Table 2 and Fig. 6 ).

Proportion and growth of the most prevalent primary research fields since the year 2000 and the percentage of selected subgroups of the “context of use” and the “text source” of the total for the subgroups of the primary research field

Proportion and growth of the “context of use” since the year 2000 and the percentage of selected subgroups of the “text source” of the total for the subgroups of the “context of use”

According to Fig. 5 , clinical decision support applications and electronic medical/health records had the highest proportion of context of use and text source for each subgroup of primary research fields. The proportion of text source and context of use subtypes varied significantly across medical fields. For instance, published papers on NLP method advancement accounted for the highest percentage (35%) of ICD-11 codes for mental, behavioral, and neurodevelopmental disorders. Similarly, social media was used more frequently (17%) in certain infectious or parasitic diseases than in any diseases designated by ICD-11 codes.

Yu Zhu et al. [ 13 ] used output-modified BioBERT pre-trained with PubMed and PMC and obtained an f-score of 80.9, like ours. Elangovan et al. [ 11 ] found a lower f-score in a similar study. The other two systematic reviews observed a similar upward trend in using NLP in various medical fields over the last two decades [ 14 , 15 ]. In 2000, medical NLP publications began to appear prominently in peer-reviewed journals. This study shows BioBERT can spot an expected result reported in previous studies.

We were particularly interested in the type of text sources used in medial NLP, the type of medical conditions studied, and the motivation behind performing NLP. Three published bibliographic studies shared some features with ours. Using PubMed data, Chen et al. examined 1405 papers over 10 years (2007–2016) and reported country-region, author-affiliation, and thematic NLP research areas [ 14 ]. Using PubMed data from 1999 to 2018, Wang et al. identified 3498 publications. Additionally, country-regions, author affiliations, disease areas, and text sources were reported [ 15 ]. Similar to Wang [ 15 ] and Chen [ 14 ], Chen et al. [ 17 ] used NLP methods to explore a similar set of variables; however, the authors focused only on NLP-enhanced clinical trial research. PubMed, Web of Science, and Scopus were searched for 451 published articles from 2001 to 2018. We selected 12,817 peer-reviewed citations using a different approach than typical bibliographic methods. We systematically scanned four chief article datasets and manually classified citations based on three variables: primary research fields, text source used, and motivation for NLP (context of use). In addition, we used BioBERT of Google as a preferred NLP method to assign subgroups to our variables.

Unlike typical bibliometric research, we were not interested in regional or institutional distributions, typical features of bibliometric research. Instead, we explored the hows and whys of medical NLP research over the past two decades.

According to our results, annual medical NLP publications grew by roughly 21% between 2000 and 2020 on average, similar to the nearly 18% growth. Chen et al. reported between 2007 and 2016 [ 14 ]. According to Wang et al. [ 15 ] and Chen et al. [ 17 ], medical NLP publications increased rapidly between 1999 and 2017. The logarithmic progression of the citations in our study can partly be explained by the annual increase of over 65% in NLP studies using omics datasets. Nearly 27% of all NLP research was conducted on neoplasms, mental conditions, infectious diseases, and circulatory diseases. Similarly, Wang et al. retrieved around 25% of their citations from neoplasms [ 15 ]. Previous authors have not explained why medical NLP citations are unequally high in cancer and a limited number of other fields, like mental health. The same is valid for why particular medical conditions are at the center of medical community researchers, while EHR (electronic health records) or EMR (electronic medical records) massive data must be equally available for all medical conditions proportional to their prevalence. In the case of cancers and infectious lung disease, however, unstructured text may convey more information because of pathology or radiology reports. We can potentially apply medical NLP to broader clinical and research settings by studying the systematic differences across medical conditions from an NLP standpoint.

There are strengths and weaknesses to our approach. We began by categorizing medical conditions hierarchically using a systemic strategy. To identify primary research fields, we used ICD-11’s top-level taxonomy. In the future, if NLP studies follow the same procedure, the findings will remain comparable. We chose the BioBERT model from various pre-trained language models, including ClinicalBERT and BlueBERT. BioBERT can train with 4.5 billion biomedical words from PubMed abstracts and 13.5 billion words from PMC full-text articles. Compared to similar BERT models, NLP researchers are more involved with BioBERT. Hence, we recommend comparing the performance of various BERT models before selecting a model if an NLP specialist is not confident enough to choose the proper model. Finally, we publish the method for developing the analysis database (NLP corpus) based on medical systematic review guidelines. Future research can use this approach to confirm whether NLP can replace systematic literature reviews.

A potential shortcoming of our study was the idiosyncratic nature of the initial classification used for training the machine. Using our experience with observational datasets, such as electronic clinical notes and NLP applications, to analyze unstructured clinical data, we began building the initial subgroups. Nevertheless, to mitigate the risk of bias, we dissected the published studies cumulatively until more studies could not update the evolving classification. Our models’ estimated classification accuracy may have been adequate because of this strategy. The model can be fine-tuned based on more annotated articles, the hyper-parameters can be tuned more thoroughly, and multi-task learning can be explored instead of training separate models for each task. Additionally, the accuracy may improve further after the training dataset is expanded. Finally, we only included abstracts written in English. Results and conclusions may be influenced if relevant research published in languages other than English is excluded.

This study aimed to evaluate the performance of BioBERT as a tool to substitute manual abstract review using a language model. BioBERT is an acceptable method for abstract selection for systematic literature searches since it uses a uniform and human-independent algorithm that reduces the time required for manual abstract selection and increases inter-study reliability.

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Data sharing is not applicable, but the authors are happy to share the abstracts publicly if that helps the reviewers or readers.

Abbreviations

Natural language processing

Average annual growth rate

Electronic health records

Electronic medical records

International Classification of Diseases

Bidirectional Encoder Representations from Transformers

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Safoora Masoumi

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Hossein Amirkhani

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Additional file 1.

: Appendix 1 . The three tables (a, b, and c) show the absolute number of abstracts retrieved between 2000 and 2020 (inclusive) for each of the three classes studied.

Additional file 2

: Appendix 2 . The search strategy used to received abstracts from four databases.

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Masoumi, S., Amirkhani, H., Sadeghian, N. et al. Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research. Syst Rev 13 , 107 (2024). https://doi.org/10.1186/s13643-024-02470-y

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Flow-Diverting Devices in the Treatment of Vertebral Artery Aneurysms: Insights into Efficacy and Safety from a Systematic Review and Meta-analysis

Published: 11 April 2024

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Changya Liu 1 na1 ,
Xinxin Wu 2 na1 ,
Kaikai Guo 3 ,
Yuting Sun 1 ,
Cai Yike 3 ,
Xuebin Hu 3 &
Bangjiang Fang 1 , 4 , 5

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The objective of this study is to conduct a systematic review and meta-analysis aimed at evaluating the efficacy and safety of flow-diverting devices (FDs) treatment for intracranial vertebral artery (VA) aneurysms. We searched PubMed, Web of Science, OVID, and Embase for English-language studies up to February 2024 and included clinical studies on FD treatment of intracranial VA aneurysms. Sensitivity analysis evaluated outcome stability. Of 2273 articles, 29 studies involving 541 aneurysms treated with FDs were included. Based on the Methodological Index for Non-Randomized Studies (MINORS), six were high-quality and 23 moderate quality. FD treatment showed a 95% rate of favorable clinical outcomes (95% CI, 89–99%), 81% (95% CI, 74–88%) complete aneurysmal occlusion, 4% (95% CI, 2–7%) ischemic complication incidence, 1% (95% CI, 0–3%) hemorrhagic complication incidence, 95% (95% CI, 87–100%) posterior inferior cerebellar artery (PICA) preservation, and 6% (95% CI, 3–10%) in-stent stenosis or occlusion across clinical and angiographic follow-up periods of 13.62 months (95% CI, 10.72–16.52) and 11.85 months (95% CI, 9.36–14.33), respectively. Subgroup analyses, based on a 12-month angiographic follow-up threshold, indicated no statistically significant differences in rates of complete aneurysm occlusion, PICA preservation, or in-stent stenosis or occlusion incidence ( p > 0.05) between subgroups. Moreover, significant differences were observed in clinical and angiographic outcomes between ruptured and unruptured aneurysms, particularly in hemorrhagic complications ( p < 0.05), without significant disparity in ischemic complications ( p > 0.05). The results’ stability was confirmed via sensitivity analysis. FDs treatment for VA aneurysms is efficacious and safe, offering high rates of positive clinical and angiographic outcomes with minimal complications, underscoring FDs’ viability as a treatment option for VA aneurysms. The study was registered with PROSPERO (registration number: CRD42024499894).

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Abbreviations

Vertebral artery

Flow-diverting devices

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Methodological Index for Non-Randomized Studies

Posterior inferior cerebellar artery

Anterior spinal artery

Parent vessel sacrifice

Pipeline embolization device

Modified Rankin Scale

Glasgow Outcome Scale

Raymond-Roy classification

O’Kelly-Marotta

Confidence interval

Standard deviation

Stent-assisted coil embolization

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Department of Emergency, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, NO.725 Wanping South Road, Xuhui District, Shanghai, 200032, China

Changya Liu, Yuting Sun & Bangjiang Fang

Central Laboratory, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, 200443, China

Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China

Kaikai Guo, Cai Yike & Xuebin Hu

Chongqing General Hospital, Chongqing University, Chongqing, 401147, China

Bangjiang Fang

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Conception and design: CL, BF, and XH; analysis and interpretation of the data: CL, XH, and XW; drafted the article: CL and XW; critically revised the article: BF and XH; reviewed submitted version of manuscript: CL and XW; final approval of the version to be published: BF, XH, and CL; technical, material support: YS, YC, and KG.

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Xuebin Hu and Bangjiang Fang have contributed equally to this work and shared last authorship.

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Liu, C., Wu, X., Guo, K. et al. Flow-Diverting Devices in the Treatment of Vertebral Artery Aneurysms: Insights into Efficacy and Safety from a Systematic Review and Meta-analysis. Transl. Stroke Res. (2024). https://doi.org/10.1007/s12975-024-01251-y

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DOI : https://doi.org/10.1007/s12975-024-01251-y

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Published: 14 November 2023

Employment of patients with rheumatoid arthritis - a systematic review and meta-analysis

Lilli Kirkeskov 1 , 2 &
Katerina Bray 1 , 3

BMC Rheumatology volume 7 , Article number: 41 ( 2023 ) Cite this article

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Patients with rheumatoid arthritis (RA) have difficulties maintaining employment due to the impact of the disease on their work ability. This review aims to investigate the employment rates at different stages of disease and to identify predictors of employment among individuals with RA.

The study was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines focusing on studies reporting employment rate in adults with diagnosed RA. The literature review included cross-sectional and cohort studies published in the English language between January 1966 and January 2023 in the PubMed, Embase and Cochrane Library databases. Data encompassing employment rates, study demographics (age, gender, educational level), disease-related parameters (disease activity, disease duration, treatment), occupational factors, and comorbidities were extracted. Quality assessment was performed employing Newcastle–Ottawa Scale. Meta-analysis was conducted to ascertain predictors for employment with odds ratios and confidence intervals, and test for heterogeneity, using chi-square and I 2 -statistics were calculated. This review was registered with PROSPERO (CRD42020189057).

Ninety-one studies, comprising of a total of 101,831 participants, were included in the analyses. The mean age of participants was 51 years and 75.9% were women. Disease duration varied between less than one year to more than 18 years on average. Employment rates were 78.8% (weighted mean, range 45.4–100) at disease onset; 47.0% (range 18.5–100) at study entry, and 40.0% (range 4–88.2) at follow-up. Employment rates showed limited variations across continents and over time. Predictors for sustained employment included younger age, male gender, higher education, low disease activity, shorter disease duration, absence of medical treatment, and the absence of comorbidities.

Notably, only some of the studies in this review met the requirements for high quality studies. Both older and newer studies had methodological deficiencies in the study design, analysis, and results reporting.

Conclusions

The findings in this review highlight the prevalence of low employment rates among patients with RA, which increases with prolonged disease duration and higher disease activity. A comprehensive approach combining clinical and social interventions is imperative, particularly in early stages of the disease, to facilitate sustained employment among this patient cohort.

Peer Review reports

Rheumatoid arthritis (RA) is a chronic, inflammatory joint disease that can lead to joint destruction. RA particularly attacks peripheral joints and joint tissue, gradually resulting in bone erosion, destruction of cartilage, and, ultimately, loss of joint integrity. The prevalence of RA varies globally, ranging from 0.1- 2.0% of the population worldwide [ 1 , 2 ]. RA significantly reduces functional capacity, quality of life, and results in an increase in sick leave, unemployment, and early retirement [ 3 , 4 , 5 ]. The loss of productivity due to RA is substantial [ 2 , 5 , 6 , 7 ]. A 2015 American study estimated the cost of over $250 million annually from RA-related absenteeism in United States alone [ 8 ].

Research has highlighted the importance of maintaining a connection to the labour market [ 3 , 9 ], Even a short cessation from work entails a pronounced risk of enduring work exclusion [ 10 ]. In Denmark merely 55% on sick leave for 13 weeks succeeded in re-joining the workforce within one year. Among those on sick leave for 26 weeks, only 40% returned to work within the same timeframe [ 11 ]. Sustained employment is associated with an improved health-related quality of life [ 12 , 13 ]. Early and aggressive treatment of RA is crucial for importance in achieving remission and a favourable prognosis reducing the impact of the disease [ 2 , 14 , 15 , 16 ]. Therefore, initiating treatment in a timely manner and supporting patients with RA in maintaining their jobs with inclusive and flexible workplaces if needed is critical [ 3 , 17 ].

International studies have indicated, that many patients with RA are not employed [ 18 ]. In 2020, the average employment rate across Organization for Economic Co-operation and Development (OECD) countries was 69% in the general population (15 to 64 years of age), exhibiting variations among countries, ranging from 46–47% in South Africa and India to 85% in Iceland [ 19 ]. Employment rates were lower for individuals with educational levels below upper secondary level compared to those with upper secondary level or higher education [ 19 ]. For individuals suffering with chronic diseases, the employment rates tend to be lower. Prognostic determinants for employment in the context of other chronic diseases encompasses the disease’s severity, employment status prior to getting a chronic disease, and baseline educational level [ 20 , 21 , 22 ]. These somatic and social factors may similarly influence employment status of patients with RA. Several factors, including the type of job (especially physically demanding occupations), support from employers and co-workers, social safety net, and disease factors such as duration and severity, could have an impact on whether patients with RA are employed [ 17 , 23 , 24 ]. Over the years, politicians and social welfare systems have tried to improve the employment rates for patients with chronic diseases. In some countries, rehabilitation clinics have been instrumental in supporting patients to remain in paid work. Healthcare professionals who care for patients with RA occupy a pivotal role in preventing work-related disability and support the patients to remain in work. Consequently, knowledge of the factors that contribute to retention of patients with RA at work is imperative [ 17 , 25 ].

The aim of this study is therefore to conduct a systematic review, with a primary focus on examining employment rates among patients with RA at the onset of the disease, at study entry, and throughout follow-up. Additionally, this study intends to identify predictors of employment. The predefined predictors, informed by the author’s comprehensive understanding of the field and specific to RA, encompass socioeconomic factors such as age, gender, level of education, employment status prior to the disease, disease stage and duration, treatment modalities, and comorbidities, including depression, which are relevant both to RA and other chronic conditions [ 26 ].

This systematic review was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) for studies that included employment rate in patients with rheumatoid arthritis [ 27 ]. PROSPERO registration number: CRD42020189057.

Selection criteria and search strategies

A comprehensive literature search was conducted, covering the period from January 1966 to January 2023 across the PubMed, Embase, and Cochrane Library databases using the following search terms: (Rheumatoid arthritis OR RA) AND (employment OR return to work). Only studies featuring a minimum cohort size of thirty patients and articles in the English language were deemed eligible for inclusion.

The initial screening of articles was based on the titles and abstracts. Studies comprising a working-age population, with current or former employment status, and with no limitations to gender, demographics, or ethnicity were included in this review. Articles addressing topics of employment, work ability or disability, return to work or disability pension were encompassed within the scope of this review. Full-time and part-time employment, but not ‘working as housewives’ was included in this review’s definition of employment. Studies involving other inflammatory diseases than RA were excluded. Reference lists in the selected articles were reviewed, and more articles were included if relevant. A review of the reference lists in the initially selected articles was conducted, with additional articles incorporated if they proved relevant to the research objectives. The eligible study designs encompassed cohort studies, case–control studies, and cross-sectional studies. All other study designs, including reviews, case series/case reports, in vitro studies, qualitative studies, and studies based on health economics were systematically excluded from the review.

Data extraction, quality assessment and risk-of-bias

The data extraction from the selected articles included author names, year of publication, study design, date for data collection, employment rate, study population, age, gender, educational level, ethnicity, disease duration, and pharmacological treatment. To ensure comprehensive evaluation of study quality and potential bias, quality assessment was independently assessed by two reviewers (LK and KB) using the Newcastle–Ottawa Scale (NOS) for cross-sectional and cohort studies [ 28 ]. Any disparities in the assessment were resolved by discussion until consensus was reached. For cross-sectional studies the quality assessment included: 1) Selection (maximum 5 points): representativeness of the sample, sample size, non-respondents, ascertainment of the risk factor; 2) Comparability (maximum 2 points); study controls for the most important, and any additional factor; 3) Outcome (maximum 3 points): assessment of outcome, and statistical testing. For cohort studies the assessment included: 1) Selection (maximum 4 points): representativeness of the exposed cohort, selection of the non-exposed cohort, ascertainment of exposure, demonstration that the outcome of interest was not present at start of study; 2) Comparability (maximum 2 points): comparability of cohorts on the basis of the design or analysis; 3) Outcome (maximum 3 points): assessment of outcome, was the follow-up long enough for outcomes to occur, and adequacy of follow up of cohorts. The rating scale was based on 9–10 items dividing the studies into high (7–9/10), moderate (4–6) or low (0–3) quality. A low NOS score (range 0–3) indicated a high risk of bias, and a high NOS score (range 7–9/10) indicated a lower risk of bias.

Analytical approach

For outcomes reported in numerical values or percentages, the odds ratio along with their 95% confidence intervals (CI) were calculated, whenever feasible. Weighted means were calculated, and comparisons between these were conducted using t-test for unpaired data. Furthermore, meta-analysis concerning the pre-determined and potentially pivotal predictors for employment status, both at disease onset, study entry, and follow-up was undertaken. The predictors included age, gender, ethnicity, level of education, duration of disease, treatment, and the presence of comorbities, contingent upon the availability of the adequate data. Additionally, attempts have been made to find information regarding on job categorizations, disease activity (quantified through DAS28; disease activity score for number of swollen joints), and quality of life (SF-36 scores ranging from 0 (worst) to 100 (best)). Age was defined as (< = 50/ > 50 years), gender (male/female), educational level college education or more/no college education), race (Caucasian/not Caucasian), job type (non-manual/manual), comorbidities (not present/present), MTX ever (no/yes), biological treatment ever (no/yes), prednisolone ever (no/yes), disease duration, HAQ score (from 0–3)), joint pain (VAS from 1–10), and DAS28 score. Age, disease duration, HAQ score, VAS score, SF36 and DAS28 were in the studies reported by mean values and standard deviations (SD). Challenges were encountered during attempts to find data which could be used for analysing predictors of employment status before disease onset, and at follow-up, as well as factors related to treatments beyond MTX, prednisolone, and biological as predictors for being employed after disease onset. Test for heterogeneity was done using Chi-squared statistics and I 2 , where I 2 below 40% might not be important; 30–60% may represent moderate heterogeneity; 50–90% substantial heterogeneity; and 75–100% considerable heterogeneity. Meta-analysis for predictors for employment and odds ratio; confidence intervals; and test for heterogeneity were calculated using the software Review Manager (RevMan, version 5.3. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014).

General description of included studies

The search yielded a total of 2277 references addressing RA its association with employment. Following the initial title screen, 199 studies were considered relevant for further evaluation. Of those, 91 studies ultimately met the inclusion criteria. Figure 1 shows the results of the systematic search strategy.

Flow chart illustrating the systematic search for studies examining employment outcome in patients with rheumatoid arthritis

Table 1 summarizes the general characteristics of the included studies. The publication year of the included studies ranged from 1971 to 2022. Among the studies, 60 (66%) adopted a cross-sectional research design [ 13 , 18 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 129 ] with a total of 41,857 participants analysing data at a specific point in time. Concurrently, 31 studies (34%) adopted a cohort design [ 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 130 ] with a total of 59,974 participants. Most of these studies exhibited a small to moderate sample size, with a median of 652 participants. Additionally, single centre studies and studies from high-income countries were predominant. Study details are shown in Table 1 .

General description of study participants

On average, patients with RA were 51 years old, with an age range spanning from 42 to 64 years. Furthermore, the female population accounted for 75.9% of the patient cohort, with a range from 41 to 92%. The duration of the disease at study entry exhibited significant variability, ranging from less than one year up to more than 18 years on average.

Employment rate

At disease onset, the employment rate was 78.8% (weighted mean, range 45.4–100), at study entry 47.0% (range 18.5–100), and during the follow-up period 40.0% (range 4–88.2), as shown in Table 2 . Notably, a comparative analysis of the employment rates between Europe and North America indicated no substantial difference ( p = 0.93). However, the comparison between Europe, North America and ‘other continents’ did yield significant differences (or nearly differences) with p -values of 0.003 and 0.08, respectively.

The employment rate exhibited no change, when comparing studies from the 1980s through to 2022. Specifically, the weighted mean for the years 1981–2000 was 49.2%, aligning closely with the corresponding figures for the years 2001–2010 (49.2%) and 2011–2022 43.6%. These findings were statistically non-significant, with p -values of 0.80 for comparison between year 1981–2000 and 2001–2010; 0.66 for 2001–2010 and 2011–2022, and 0.94 for 1981–2000 and 2011–2022, shown in Figure S 1 , see Additional file.

Among the studies included in the analysis, nineteen studies included data of employment at follow-up, with durations ranging from 1 to 20 years, Table 2 . For instance, Jäntti, 1999 [ 97 ] reported an employment rate 69% one year after disease onset, which gradually declined to 50% after 15 years and further to 20% after 20 years. Similarly, Mäkisara, 1982 [ 63 ] demonstrated that 60% of the patients were employed 5 years after disease onset, 50% after 10 years, and 33% after 15 years. Nikiphorou, 2012 [ 101 ] reported an employment rate of 67% at study entry, which decreased to 43% after 10 years.

In addition, seven studies included data of employment rate among patients comparing different medical treatments [ 18 , 44 , 56 , 91 , 105 , 110 , 119 ]. These studies indicated that, on average, 55.0% (weighted mean) of the patients were employed after receiving treatment with MTX, while 42.8% after undergoing treatment with a combination of MTX + Adalimumab (all patients were employed at disease onset in these specific studies).

Predictors for employment

Information of normative comparison data to use for meta-analysis of predictors for employment at study entry was available for age, gender, educational level, race, job type, comorbidities, MTX at any time, biological treatment at any time, prednisolone at any time, disease duration, HAQ score, joint pain (VAS-score), and disease activity (DAS28 score). Predictors for employment at study entry was being younger /age below 50 years, being a male, higher educational level (college or more), non-manual work, having no comorbidities, no medical treatment, short disease duration, and low HAQ score, VAS-score, or DAS28 score. Heterogeneity was small for age, gender, medical treatment, and moderate for educational level, and job type as indicted by the I 2 values, Table 3 , and shown in detail in Figures S 2 , S 3 , S 4 , S 5 , S 6 , S 7 , S 8 , S 9 , S 10 , S 11 , S 12 , S 13 , S 14 , S 15 and S 16 , see Additional file.

Assessment of quality of included studies

All studies were subject to rigorous quality assessment. These assessments resulted in categorisation of either medium quality ( n = 64; 70%) or high-quality studies ( n = 27; 30%), with no studies falling into the low-quality category. The quality assessment is shown in Tables 4 and 5 .

Notably, many studies were characterised by several common attributes, including cross-sectional study design, single-centre-settings, relatively small sample sizes, and the reliance on self-reported patient data. When including only the high-quality studies in the analyses, the employment rates at study entry changed from 47% (weighted mean, all studies) to 50% (weighted mean, high quality studies).

Key findings

This systematic review has identified a decline in the employment rate among patients with RA, with a notable decrease from disease onset during the study entry to follow-up, where only half of the patients were employed. These findings corroborate earlier research that indicated a substantial decline in employment rates among patients with RA over time. Notably, previous studies have reported that approximately one third of patients with RA stopped working within 2 to 3 years after disease onset, and more than half was unable to work after 10 to 15 years [ 23 , 63 , 93 , 97 , 101 ]. Only few studies have included data from the general population, comparing the employment rates with the rates for patients with RA [ 89 , 90 ]. Comparisons with the general population further underscored the challenges faced by RA patients, as their employment rates were consistently lower.

Despite changes in medical treatment, social security systems, and societal norms over the past decades, there was no significant improvement in the employment for patients with RA. This pattern aligns with data from the Global Burden of Disease studies, highlighting the persistent need for novel approaches and dedicated efforts to support patients with RA in sustaining employment [ 2 , 123 ]. Recent recommendations from EULAR (European Alliance of Associations for Rheumatology) and ACR (American College of Rheumatology) have emphasized the importance of enabling individuals with rheumatic and musculoskeletal diseases to engage in healthy and sustainable work [ 17 , 124 , 125 ].

While different countries possess different social laws and health care systems for supporting patients with chronic diseases, the variations in the weighted mean of employment rates across countries were relatively minor.

In the meta-analysis, one of the strongest predictors for maintaining employment was younger age at disease onset [ 43 , 51 , 101 , 116 ]. Verstappen, 2004 found that older patients with RA had an increased risk of becoming work disabled, potentially caused by the cumulative effects of long-standing RA, joint damage, and diminished coping mechanisms, compared to younger patients [ 23 ].

More women than men develop RA, however this study showed that a higher proportion of men managed to remain employed compared to women [ 18 , 36 , 42 , 43 , 46 , 62 , 71 , 89 , 101 , 116 ]. Previous studies have shown inconsistent results in this regard. Eberhart, 2007 found that a significantly higher number of men with RA worked even though there was no difference in any disease state between the sexes [ 93 ]. De Roos,1999 showed that work-disabled women were less likely to be well-educated and more likely to be in a nonprofessional occupation than working women. Interestingly, there was no association of these variables among men. Type of work and disease activity may influence work capacity more in women than in men [ 46 ]. Sokka, 2010 demonstrated a lower DAS28 and HAQ-score in men compared to women among the still working patients with RA, which indicated that women continued working at higher disability and disease activity levels compared with men [ 18 ].

Disease duration also played a significant role as a predictor of employment outcomes [ 33 , 36 , 45 , 71 , 77 , 86 , 102 , 111 ]. Longer disease duration correlate with decreased employment likelihood, which could be attributed to older age and increased joint damage and disability in patients with longer-standing RA.

Higher educational levels were associated with a greater possibility of employment [ 30 , 43 , 45 , 46 , 51 , 62 , 86 ]. This is probably due to enhanced job opportunities, flexibility, lower physical workload, better insurance coverage, and improved health care for well-educated individuals. This is further supported by the fact that having a manual work was a predictor for not being employed [ 30 , 39 , 43 , 44 , 45 ].

Furthermore, health-related quality of life, as measured by SF 36, lower disease activity (DAS28 scores), reduced joint pain (VAS-score), and lower disability (HAQ score) were additionally predictors for being employed [ 33 , 35 , 36 , 45 , 71 , 86 ]. This support the statement that the fewer symptoms from RA, the greater the possibility of being able to work.

The results showed that the presence of comorbidity was a predictor for not being employed, aligning with findings from previous studies that chronic diseases such as cardiovascular disease, lung disease, diabetes, cancer, and depression reduced the chances of being employed [ 126 ]. Moreover, the risk of exiting paid work increased with multimorbidity [ 127 ].

While limited data were available for assessing the impact of treatment on employment, indications suggested that patients with RA were receiving medical treatments, such as MTX or biological medicine, were more likely to be unemployed. One possible explanation for this phenomenon could be that patients with RA, who were receiving medical treatment, had a more severe and a longer duration of RA compared to those, who had never been on medical treatment. However, the scarcity of relevant studies necessitates caution when drawing definitive conclusions in this regard.

Therefore, the predictors for employment found in this review were being younger, being a male, having higher education, low disease activity, low disease duration, and being without comorbidities. This is supported by previous studies [ 93 , 116 ]

In summary, this review underscores the importance of managing disease activity, offering early support to patients upon diagnosis, and reducing physically demanding work to maintain employment among patients with RA. Achieving success in this endeavour requires close cooperation among healthcare professionals, rehabilitation institutions, companies, and employers. Furthermore, it is important that these efforts are underpinned by robust social policies that ensure favourable working conditions and provide financial support for individuals with physical disabilities, enabling them to remain active in the labour market.

Strengths and limitations

The strength of this review and meta-analysis lies in the inclusion of a large number of articles originating from various countries. Furthermore, the data showed a consistent employment rate in high quality studies compared to all studies. However, there are some limitations to this review. No librarian was used to define search terms and only three databases were searched. Furthermore, the initial search, selection of articles, data extraction, and analysis was undertaken only by one author, potentially leading to the omission of relevant literature and data. The review also extended back to 1966, with some articles from the 1970s and 1980s included. Given the significant changes in medical treatment, social security systems, and society over the past decades, the generalizability of the findings may be limited.

Moreover, the majority of studies did not include a control group from the general population, which limited the ability to compare employment rates with the general population in the respective countries. Many studies were cross-sectional in design, which limits the evidence of causality between employment rate and having RA. However, the employment rate was approximately the same in high quality studies compared to all studies, which supports an association. A substantial number of studies relied on self-reported employment rates, introducing the potential for recall bias. Additionally, many studies did not account for all relevant risk factors for unemployment failing to control for all relevant confounders.

EULAR have made recommendation for point to consider when designing, analysing, and reporting of studies with work participation as an outcome domain in patients with inflammatory arthritis. These recommendations include study design, study duration, and the choice of work participation outcome domains (e.g., job type, social security system) and measurement instruments, the power to detect meaningful effects, interdependence among different work participation outcome domains (e.g., between absenteeism and presentism), the populations included in the analysis of each work participation outcome domain and relevant characteristics should be described. In longitudinal studies work-status should be regularly assessed and changes reported, and both aggregated results and proportions of predefined meaningful categories should be considered [ 128 ]. Only some of the studies in this review met the requirements for high quality studies. In both older and newer studies methodological deficiencies persisted in study design, analysis, and reporting of results, as recommended by EULAR.

Perspectives for future studies

Future research in this area should focus on developing and evaluating new strategies to address the ongoing challenges faced by patients with RA in maintaining employment. Despite many initiatives over the years, there has been no success in increasing employment rates for patients with RA in many countries. Therefore, there is a pressing need for controlled studies that investigated the effectiveness of interventions such as education, social support, and workplace adaptations in improving employment outcomes for these individuals.

This systematic review underscores the low employment rate among patients with RA. Key predictors of sustained employment include being younger, having higher educational level, short disease duration, and lower disease activity, along with fewer comorbidities. Importantly, the review reveals that the employment rate has not changed significantly across different time periods. To support patients with RA in maintaining their employment, a comprehensive approach that combines early clinical treatment with social support is crucial. This approach can play a pivotal role in helping patients with RA stay connected to the labour market.

Availability of data and materials

The datasets used and/or analyzed during the current study are available in the supplementary file.

Abbreviations

Rheumatoid arthritis

Methotrexate

Newcastle Ottawa Quality Assessment Scale

Standard deviation

Not analyzed

Not relevant

Disease activity

Health Assessment Questionnaire

Visual analog scale for pain

European Alliance of Associations for Rheumatology

American College of Rheumatology

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Department of Social Medicine, University Hospital Bispebjerg-Frederiksberg, Copenhagen, Denmark

Lilli Kirkeskov & Katerina Bray

Department of Social Medicine, University Hospital Bispebjerg-Frederiksberg, Nordre Fasanvej 57, Vej 8, Opgang 2.2., 2000, Frederiksberg, Denmark

Lilli Kirkeskov

Department of Occupational and Social Medicine, Holbaek Hospital, Holbaek, Denmark

Katerina Bray

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LK performed the systematic research, including reading articles, performed the blinded quality assessment and the meta-analysis, and drafted and revised the article. KM performed the blinded quality assessment and the discussion afterwards of articles to be included in the research and the scores, and drafted and revised the article.

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Supplementary Information

Additional file 1: figure s1..

Employment; year of investigation.

Additional file 2: Figure S2.

Forest Plot of Comparison: Predictors for employment. Outcome: Younger or older age.

Additional file 3: Figure S3.

Forest Plot of Comparison: Predictors for employment. Outcome: >50 yr or <50 yr of age.

Additional file 4: Figure S4.

Forest Plot of Comparison: Predictors for employment. Outcome: Gender: Male or Female.

Additional file 5: Figure S5.

Forest Plot of Comparison: Predictors for employment. Outcome: Educational level: no college education or college education or higher.

Additional file 6: Figure S6.

Forest Plot of Comparison: Predictors for employment. Outcome: no comorbidities present or one or more comorbidities present.

Additional file 7: Figure S7.

Forest Plot of Comparison: Predictors for employment. Outcome: Ethnicity: Caucasian or other than Caucasian.

Additional file 8: Figure S8.

Forest Plot of Comparison: Predictors for employment. Outcome: Short or long disease duration.

Additional file 9: Figure S9.

Forest Plot of Comparison: Predictors for employment. Outcome: Low or high Health Assessment Questionnaire, HAQ-score.

Additional file 10: Figure S10.

Forest Plot of Comparison: Predictors for employment. Outcome: Low or high VAS-score.

Additional file 11: Figure S11.

Forest Plot of Comparison: Predictors for employment. Outcome: Job type: blue collar workers or other job types.

Additional file 12: Figure S12.

Forest Plot of Comparison: Predictors for employment. Outcome: No MTX or MTX.

Additional file 13: Figure S13.

Forest Plot of Comparison: Predictors for employment. Outcome: No biological or biological.

Additional file 14: Figure S14.

Forest Plot of Comparison: Predictors for employment. Outcome: No prednisolone or prednisolone.

Additional file 15: Figure S15.

Forest Plot of Comparison: Predictors for employment. Outcome: Low or high DAS score.

Additional file 16: Figure S16.

Forest Plot of Comparison: Predictors for employment. Outcome: Low or high SF 36-score.

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Kirkeskov, L., Bray, K. Employment of patients with rheumatoid arthritis - a systematic review and meta-analysis. BMC Rheumatol 7 , 41 (2023). https://doi.org/10.1186/s41927-023-00365-4

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Mapping ethical issues in the use of smart home health technologies to care for older persons: a systematic review

Nadine Andrea Felber ORCID: orcid.org/0000-0001-8207-2996 1 ,
Yi Jiao (Angelina) Tian ORCID: orcid.org/0000-0003-2969-9655 1 ,
Félix Pageau ORCID: orcid.org/0000-0002-4249-7399 2 ,
Bernice Simone Elger ORCID: orcid.org/0000-0003-0857-0510 1 &
Tenzin Wangmo 1

BMC Medical Ethics volume 24 , Article number: 24 ( 2023 ) Cite this article

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The worldwide increase in older persons demands technological solutions to combat the shortage of caregiving and to enable aging in place. Smart home health technologies (SHHTs) are promoted and implemented as a possible solution from an economic and practical perspective. However, ethical considerations are equally important and need to be investigated.

We conducted a systematic review according to the PRISMA guidelines to investigate if and how ethical questions are discussed in the field of SHHTs in caregiving for older persons.

156 peer-reviewed articles published in English, German and French were retrieved and analyzed across 10 electronic databases. Using narrative analysis, 7 ethical categories were mapped: privacy, autonomy, responsibility, human vs. artificial interactions, trust, ageism and stigma, and other concerns.

The findings of our systematic review show the (lack of) ethical consideration when it comes to the development and implementation of SHHTs for older persons. Our analysis is useful to promote careful ethical consideration when carrying out technology development, research and deployment to care for older persons.

Registration

We registered our systematic review in the PROSPERO network under CRD42021248543.

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Introduction/background

Significant advancements in medicine, public health and technology are allowing the world population to grow increasingly older adding to the steady rise in the proportion of senior citizens (aged over 65) [ 1 ]. Because of this growth in the aging population, the demand for and financial costs of caring for older adults are both rising [ 2 ]. That older persons generally wish to age in place and receive healthcare at home [ 2 ] may mean accepting risks such as falling, a risk that increases with frailty [ 3 ]. However, many prefer accepting these risks rather than moving into long term care facilities [ 4 , 5 , 6 ].

A solution to this multi-facetted problem of ageing safely at home and receiving appropriate care, while keeping costs at bay may be the use of smart home health technologies (SHHTs). A smart home is defined by Demiris and colleagues as “ residence wired with technology features that monitor the well-being and activities of their residents to improve overall quality of life, increase independence and prevent emergencies” [ 7 ]. SHHTs then, represent a certain type of smart home technology, which include non-invasive, unobtrusive, interoperable and possibly wearable technologies that use a concept called the Internet-of-Things (IoT) [ 8 ]. These technologies could thereby remotely monitor the older resident and register any abnormal deviations in the daily habits and vital signs while sending alerts to their formal and informal caregivers when necessary. These SHHTs could permit older people (and their caregivers) to receive the necessary medical support and attention at their convenience and will, thereby allowing them to continue living independently in their home environment.

All of these functions offer benefits to older persons wishing to age at home. While focusing on practical advantages is important, an equally important question to ask is how ethical these technologies are when used in the care of older persons. Principles of biomedical ethics, such as autonomy, justice [ 9 ], privacy [ 10 ], and responsibility [ 11 ] should not only be respected by medical professionals, but by technology developers and build-into the technologies as well.

The goal of our systematic review is therefore to investigate whether and which ethical concerns are discussed in the pertinent theoretical and empirical research on SHHTs for older persons between 2000 and 2020. Different from previous literature reviews [ 12 , 13 , 14 ],, which only explored practical aspects, we explicitly examined if and how researchers treated the ethical aspects of SHHTs in their studies, adding an important, yet often overlooked aspect to the systematic literature. Moreover, we present how and which ethical concerns are discussed in the theoretical literature and which ones in empirical literature, to shed light on possible gaps regarding which and how different ethical concerns are developed. Identifying these gaps is the first important step to eventually connecting bioethical considerations to the real world, adapting policies, guidelines and technologies itself [ 15 ]. Thus, our systematic review is the first one to do so in the context of ethical issues in SHHTs used for caregiving for older persons.

Search strategy

With the guidance of an information specialist from the University of Basel, our team developed a search strategy according to the PICO principle: Population 1 (Older adults), Population 2 (Caregivers), Intervention (Smart home health technologies), and Context (Home). The outcome of ethics was intentionally omitted as we wanted to capture all relevant studies without narrowing concerns that we would classify as “ethical”. Within each category, synonyms and spelling variations for the keywords were used to include all relevant studies. We then adapted the search string by using database-specific thesaurus terms in all ten searched electronic databases: EMBASE, Medline, PsycINFO, CINAHL, SocIndex, SCOPUS, IEEE, Web of Science, Philpapers, and Philosophers Index. We limited the search to peer-reviewed papers published between January 1st, 2000 and December 31st, 2020, written in the English, French, and German languages. This time frame allowed us to map the evolution to SHHTs as a new field.

The inclusion criteria were the following: (1) The article must be an empirical or theoretical original research contribution. Hence, book chapters, conference proceedings, newspaper articles, commentary, dissertations, and thesis were excluded. Also excluded were other systematic reviews since their inclusion would duplicate findings from our individual studies. (2) When the included study was empirical, the study’s population of interest must be older persons over 65 years of age, and/or professional or informal caregivers who provide care to older persons. Informal caregivers include anyone in the community who provided support without financial compensation. Professional caregivers include nurses and related professions who receive financial compensation for their caregiving services. (3) The included study must investigate SHHTs and their use in the older persons’ place of dwelling.

First, we carried out the systematic search across databases and removed all duplicates through EndNote (see supplementary Table 1 in appendix part 1 for a list of all included articles). One member of the research team screened all titles manually and excluded irrelevant papers. Then, two authors screened the abstracts and excluded irrelevant papers, and any disagreements were solved by a third author. She then also combined all included articles and removed further duplicates.

PRISMA 2020 Flowchart

Final inclusion and data extraction

All included articles were searched and retrieved online (and excluded if full text was not available). Three co-authors then started data extraction, where several papers were excluded due to irrelevant content. To code the extracted data, a template was developed, which was tested in a first round of data extraction and then used in Microsoft Excel during the remaining extraction process. Study demographics and ethical considerations were recorded. Each extracting author was responsible for a portion of articles. If uncertainties or disputes occurred, they were solved by discussion. To ensure that our data extraction was not biased, 10% of the articles were reviewed independently. Upon comparing data extracted of those 10% of our overall sample, we found that items extracted reached 80% consistency.

Data synthesis

The extracted datasets were combined and ethical discussions encountered in the publications were analyzed using narrative synthesis [ 16 ]. During this stage, the authors discussed the data and recognized seven first-order ethical categories. Information within these categories were further analyzed to form sub-categories that describe and/or add further information to the key ethical category.

Nature of included articles

Our search initially identified 10,924 papers in ten databases. After the duplicates were removed, 9067 papers remained whose titles were screened resulting in exclusion of 5215 papers (Fig. 1 ). The examination of remaining 3845 abstracts of articles led to the inclusion of 374 papers for full-texts for retrieval. As we were unable to find 20 papers after several attempts, the remaining 354 full-texts were included for full-text review. In this full-text review phase, we further excluded 198 full-texts with reasons (such as technologies employed in hospitals, or technologies unrelated to health). Ultimately, this systematic review included 144 empirical and 12 theoretical papers specifying normative considerations of SHHTs in the context of caregiving for older persons.

Almost all publications (154 out of 156) were written in English, and over 67% [ 105 ] were published between 2014 and 2020. About a quarter (26%; 41 papers) were published between 2007 and 2013 and only 7% (10 articles) were from 2000 to 2006. Apart from the 12 theoretical papers, the methodology used in the 144 empirical papers included the following: 42 articles (29%) used a mixed-methods approach, 39 (27%) experimental, 38 (26%) qualitative, 15 (10%) quantitative, and the remaining were of an observational, ethnographical, case-study, or iterative testing nature.

The functions of SHHTs tested or studied in the included empirical papers were categorized as such: 29 articles (20.14%) were solely involved with (a) physiological and functioning monitoring technologies, 16 (11.11%) solely with (b) safety/security monitoring and assistance functions, 23 (15.97%) solely promoted (c) social interactions, and 9 (6.25%) solely for (d) cognitive and sensory assistance. However, 46 articles (29%) also involved technologies that fulfilled more than one of the categorized functions. The specific types of SHHTs included in this review comprised: intelligent homes (71 articles, 49.3%); assistive autonomous robots (49 articles, 34.03%); virtual/augmented/mixed reality (7, 4.4%); and AI-enabled health smart apps and wearables (4 articles, 1.39%). Likewise, the remaining 20 articles (12.8.8%) involved either multiple technologies or those that did not fall into any of the above categories.

Ethical considerations

Of the 156 papers included, 55 did not mention any ethical considerations (See supplementary Table 1 in appendix part 1). Among the 101 papers that noted one or more ethical considerations, we grouped them into 7 main categories (1) privacy, (2) human vs. artificial relationships, (3) autonomy, (4) responsibility, (5) social stigma and ageism, (6) trust, and (7) other normative issues (see Table 1 ). Each of these categories consists of various sub-categories that provided more information on how smart home health technologies (possibly) affected or interacted with the older persons or caregivers in the context of caregiving (Table 2 ). Each of the seven ethical considerations are explained in depth in the following paragraphs.

This key category was cited across 58 articles. In theoretical articles, privacy was one of the most often discussed ethical consideration, as 9 out of 12 mentioned privacy related concerns. Among the 58 articles, four sub-issues within privacy were discussed.

(A)The awareness of privacy was reported as varying according to the type of SHHT end-user. Whereas some end-users were more aware or privacy in relation to SHHTs, others denoted little or a total lack of consideration, while some had differing levels of concerns for privacy that changed as it is weighed against other values, such as access to healthcare [ 17 ] or feeling of safety [ 18 ]. Both caregivers and researchers often took privacy concerns into account [ 19 , 20 , 21 ], while older persons themselves did not share the same degree of fears or concerns [ 22 , 23 , 24 ]. Older persons in fact were less concerned about privacy than costs and usability [ 23 ]. Furthermore, they were willing to trade privacy for safety and the ability to live at home. Nevertheless, several papers acknowledged that privacy is an individualized value, whereby its significance depends on both the person and their context, thus their preferences cannot be generalized [ 25 , 26 , 27 , 28 ]. Lastly, there were also some papers that explicitly stated that there were no privacy concerns found by the participants, or that participants found it useful to have monitoring without mentioning privacy as a barrier [ 29 , 30 , 31 ].

The second prevalent sub-issue within privacy was (B) privacy by choice. Both older persons and their caregivers expressed a preference for having a choice in technology used, in what data is collected, and where technology should or should not be to installed [ 32 , 33 ]. For example, some spaces were perceived as more private and thus monitoring felt more intrusive [ 34 , 35 , 36 ]. Formal caregivers were concerned about monitoring technologies being used as a recording device for their work [ 37 , 38 ]. Furthermore, older persons were often worried about cameras [ 39 , 40 ] and “eyes watching”, even if no cameras were involved [ 41 , 42 , 43 ].

The third privacy concern was (C) risk and regulation of privacy, which included discussions surrounding dissemination of data or active data theft [ 44 , 45 , 46 , 47 ], as well as change in behavior or relationships due to interaction with technology [ 48 , 49 ]. Researchers were aware of both legal and design-contextual measures that must be observed in order to ensure that these risks were minimized [ 45 , 50 , 51 ].

The final sub-issue that we categorized was (D) privacy in the case of cognitive impairment. This included disagreements if cognitive impairment warrants more intrusive measures or if privacy should be protected for everyone in the same way [ 52 , 53 ].

Human versus artificial relationships

54 articles in our review contained data pertinent to trade-offs between human and artificial caregiving. Firstly, (A) there was a general fear that robots would replace humans in providing care for older persons [ 28 , 54 , 55 , 56 ], along with related concerns such as losing jobs [ 40 , 57 ], disadvantages with substituting real interpersonal contact [ 17 , 46 ], and thus increasing the negative effects associated with social isolation [ 41 , 58 ].

Many papers also emphasized (B) the importance of human caregiving, underlining the necessity of human touch [ 26 , 47 , 50 , 59 ] believing that technology should and could not replace humans in connections [ 17 ], love [ 33 ], relationships [ 60 ], and care through attention to subtle signs of health decline in every in-person visit [ 57 ]. Older persons also preferred human contact over machines and had guarded reactions to purely virtual relationships[ 31 , 61 , 62 ]. The use of technology was seen to dehumanize care, as care should be inherently human-oriented [ 27 , 48 ].

There was data alluding to (C) the positive reactions to technologies performing caregiving tasks and possibly forming attachments with the technology[ 47 , 49 , 58 ]. Furthermore, some papers cited participants reacting positively to robots replacing human care, where the concept of “good care” could be redefined [ 63 , 64 , 65 , 66 ]. Solely theoretical papers also identified possible benefits of tech for socialization and relationship building [ 67 , 68 ].

Finally, many articles raised the idea of (D) collaboration between machine and human to provide caregiving to older persons [ 69 ]. These studies highlighted the possible harms if such collaboration was not achieved, such as informal caregivers withdrawing from care responsibilities [ 70 ] or the reinforcement of oppressive care relations [ 71 ]. Interestingly, opinions varied on whether the caregiving technology, such as a robot should have “life-like” appearance, voices, and emotional expressions, while recognizing the current technological limits in actually providing those features to a satisfactory level [ 46 ]. For example, some users preferred for the robot to communicate with voice commands, while others wanted to further customize this function with specific requests on the types of voices generated [ 65 , 72 ].

40 papers mentioned autonomy of the older person with respect to the use of SHHTs. The first sub-theme categorized was in relation to (A) control, which encompassed positive aspects like (possible) empowerment through technology [ 25 , 26 , 73 , 74 ] and negative aspects such as the possibility of technology taking control over the older person, thus increasing dependence [ 55 , 75 ] or decreasing freedom of decision making [ 48 ]. Several studies reported the wishes of older persons to be in control when using the technology (e.g. technology should be easily switched off or on) and be in control of its potential, meaning the extend of data collected or transferred, for example [ 17 , 30 , 70 , 76 ]. Furthermore, they should have the option to not use technology in spaces where they do not wish to, e.g., public spaces [ 35 ]. The issue of increased dependency was discussed as a loss or rather, fear of the loss of autonomy due to greater reliance on technology as well as the fear of being monitored all the time [ 28 , 48 ]. In addition, using technology was deemed to make older persons more dependent and to increase isolation [ 77 ].

The second sub-category within autonomy highlighted the need for the technology to (B) protect the autonomy and dignity of its older end-users, which also included the unethical practice of deception (e.g.[ 46 , 49 , 54 , 78 ], infantilization [ 31 , 60 ], or paternalism [ 17 , 27 , 57 ], as a way to disrespect older persons’ dignity and autonomy [ 79 , 80 , 81 ]. Also reported was that these users may accept technology to avoid being a burden on others, thus underscoring the value of technology to enhance functional autonomy, understood here as independent functioning [ 52 , 82 , 83 ]. Other studies mentioned this kind of trade-off between autonomy and other values or interests as well. For example, between respecting the autonomy of the older persons versus nudging them towards certain behavior (perceived as beneficial for them) through the help of technology [ 32 ], or between autonomy and safety [ 24 ].

Two sub-issues within autonomy primarily discussed in the theoretical publications were (C) relational autonomy [ 27 , 41 , 49 , 58 ] and (D) explanations on why autonomy should actually be preserved. The former emphasized the fact that older persons do not and should not live isolated lives and that there should be respect and promotion of their relationships with family members, friends, caregivers, and the community as a whole [ 27 , 47 ]. The latter described the benefits of respecting autonomy, such as increased happiness and well-being [ 65 , 67 ] or a sense of purpose [ 84 ], and thus favoring the promotion of autonomy and choice also from a normative perspective.

Responsibility

This theme included data across 25 articles that mentioned concerns such as the effect of using technologies on the current responsibilities of caregivers and older persons themselves. Specifically, the papers discussed (A) the downsides of assistive home technology on responsibility. That is, the use of technology conflicted with moral ideas around responsibility [ 58 ], especially for caregivers [ 57 , 59 ]. Its use also raised more practical concerns, such as the fear of shifting the responsibility onto the technology and thus, diminishing vigilance and/or care. Related to this thought was also a fear of increased responsibility on both older persons [ 60 ] and their caregivers, who were worried about extra work time was needed to integrate technology into their work, learn its functions, analyze data, and respond to potentially higher frequencies of alerts [ 18 , 35 , 36 , 53 , 85 ].

Additionally, studies reported (B) continuous negotiation between (formal) caregivers’ (professional) responsibilities of care and the opportunities that smart technologies could provide [ 26 , 47 , 55 , 70 , 82 ]. For example, increased need for cooperation between informal and formal caregivers due to technology was foreseen [ 81 ] and fear expressed that over-reliance on female caregivers was exacerbated [ 71 ]. Nevertheless, the use of smart home health technologies was often seen to (C) reduce the burden of care, where caregivers could direct their attention and time to the most-needed situations and better align the responsibilities of care [ 5 , 18 , 49 , 74 , 80 , 81 ]. This shift of burden onto a technology was also reported by older persons as freeing [ 48 ].

Ageism and stigma

24 articles discussed ageism and stigma, which included discussions about fear of (A) being stigmatized by others with the use of SHHTs [ 73 , 86 ]. Older persons thought acceptance of such technologies also alluded to an admission of failure [ 82 ], or being perceived by others as frail, old, forgetful [ 77 , 87 ], or even stupid [ 26 , 33 , 88 ]. This resulted in them expressing ageist views stating that they did not need the technology “yet” [ 84 , 89 ]. Some papers reported the belief that the presence of robots was disrespectful for older people [ 52 , 85 , 90 ] and technologies do little to alleviate frustration and the impression of “being stupid” that older persons may have when they are faced with the complexities of the healthcare system [ 73 ]. Furthermore, older persons in a few studies did express unfamiliarity with learning new technologies in old age [ 42 , 66 , 91 ], coupled with fears of falling behind and not keeping up with their development, and feeling pressured to use technology [ 62 , 89 ].

Within ageism and stigma, (B) social influence was deemed to cause older persons to believe that the longer they have been using technology, the more their loved ones want them to use it as well, creating a sort of reinforcing loop [ 27 ]. Other social points were related to self-esteem, meaning that older persons needed to reach a certain threshold first to publicly admit that they need technology [ 85 ], or doubts by caregivers if they were able to use the devices [ 36 ]. This possibly led older persons to prefer unobtrusive technology and those that could not be noticed by visitors [ 22 , 55 , 88 ].

Lastly, (C) two theoretical articles raised concerns in regard to technology exacerbating stigmatization of women and migrants in caregiving. Both Parks [ 47 ] and Roberts & Mort [ 71 ] suggested that caregiving technology which does not question the underlying expectation that women give care to their relatives will worsen such gendered expectations in caregiving.

We identified 18 articles that mentioned some aspect of trust. For both older persons and caregivers, there was often (A) a general mistrust with technologies compared with existing human caregiving [ 33 , 42 ]. Therefore, caregivers became proxies and were relied on to “understand it” and continue providing care [ 48 ]. For caregivers the lack of trust was associated with the use of technologies, for example, leaving older persons alone with technology [ 81 ], worrying that older persons would not trust the technology [ 29 , 32 ] or that it could change their professional role [ 23 ]. One paper even reported that using technology meant caregivers themselves are not trusted [ 92 ]. Surprisingly, some studies found that older persons had no problem trusting technology, even considering it safer and more reliable than humans [ 58 , 70 ].

The second sub-theme concerned (B) characteristics promoting trust. That is, the degree of automation [ 30 ](, the involvement of trusted humans in design and use [ 34 , 93 ], perceived usefulness of the technology and spent time with the technology all influenced trust [ 59 , 72 , 94 ]. For robots specifically, they were trusted more than virtual agents, such as Alexa [ 60 , 65 ]. Taking this step further, studies discovered that robots with a higher degree of automation or a lower degree in anthropomorphism level increased trust [ 30 ].

There were several miscellaneous considerations not fitting the ones already mentioned above, and we categorized them as follows. Firstly, two theoretical articles mentioned (A) considerations related to research. Ho, [ 27 ] pointed out that empirical evidence of the usefulness of SHHTs is lacking, which therefore may make them less relevant as a possible solution for aging in place. Palm et al. (2013) suggested that, if research would consider the fact that many costs of caregiving are hidden because of non-paid informal caregivers, the actual economic benefits of SHHTs are unknown. Lastly, two articles alluded to (B) psychological phenomena related to the use of SHHTs. Pirhonen et al., [ 58 ] suggested that robots can promote the ethical value of well-being through the promotion of feelings of hope. The other phenomenon was feeling of blame and fear associated with the adoption of the technology, as caregivers may be pushed to use SHHTs in order to not be blamed for failing to use technology [ 18 ]. This then also nudged caregivers to think that using SHHTs cannot do any harm, so it is better to use it than not use it.

Our systematic review investigated if and how ethical considerations appear in the current research on SHHTs in the context of caregiving for older persons. As we included both empirical and theoretical works of literature, our review is more comprehensive that existing systematic reviews (e.g.[ 12 , 13 , 14 ], that have either only explored the empirical side of the research and neglected to study ethical concerns. Our review offers an informative and useful insights on dominant ethical issues related to caregiving, such as autonomy and trust [ 95 , 96 ]. At the same time, the study findings brings forth less known ethical concerns that arise when using technologies in the caregiving context, such as responsibility [ 97 ] and ageism and stigma.

The first key finding of our systematic review is the silence on ethics in SHHTs research for caregiving purposes. Over a third of the reviewed publications did not mention any ethical concern. One possible explanation is related to scarcity [ 98 ]. In the context of research in caregiving for older persons, “scarcity” can be understood in a variety of ways: one way is to see the available space for ethical principles in medical technology research as scarce. For example, according to Einav & Ranzani [ 99 ] “Medical technology itself is not required to be ethical; the ethics of medical technology revolves around when, how and on whom each technology is used” (p.1612). Determining the answers to these questions is done empirically, by providing proof of benefit of the technology, ongoing reporting on (possibly harmful) long term effects, and so on [ 99 ]. Given that publication space in journal is limited to a certain amount of text, the available space that ethical considerations can take up is scarce. Therefore, adding deliberations about the unearthed values or issues in our systematic review, like trust, responsibility or ageism, may simply not fit in the space available in research publications. This may also be the reason why the values of beneficence and non-maleficence were not found through our narrative analysis. While both values are considered crucial in biomedical ethics [ 9 ], the empirically measured benefits may be considered enough by the authors to demonstrate beneficence (and non-maleficence), leading them to not mention the ethical values explicitly again in their publications.

Another interpretation is the scarcity of time, and the felt pressure to “solve” the problem of limited resources in caregiving [ 2 ]. Researchers might be therefore more inclined to focus on the empirical data showing benefits, rather than to engage in elaborations on ethical issues that arise with those benefits. Lastly, as researchers have to compete for limited funding [ 100 ] and given that technological research receives more funding than biomedical ethics [ 101 ], it is likely that the numbers of publications mentioning purely empirical studies exceeds those publications that solely mention the ethical issues (as our theoretical papers did) or that combine empirical and ethical parts. Further research needs to investigate these hypotheses further.

It is not surprising that privacy was the most discussed ethical issue in relation to SHHTs in caregiving. The topic of privacy, especially in relation to monitoring technologies and/or health, has been widely discussed (see for example [ 102 , 103 , 104 ]. A particularly interesting finding within this ethical concern was related to privacy and cognitive impairment. While discussions around autonomy and cognitive impairment are popular in bioethical research (see e.g. [ 105 , 106 ], privacy, on the other hand, has recently gained more attention for both researchers and designers [ 107 ]. The relation in the reviewed studies between cognitive impairment and privacy seemed to be reversely correlated –intrusions into the privacy of older persons with cognitive impairments were deemed as more justified [ 35 , 53 ], which necessarily does not mean that its ethical, but a practical fact that such intrusions become possible or necessary in the given context. A possible explanation lies in the connectedness of autonomy and privacy, in the sense that autonomy is needed to consent for any sort of intrusions [ 108 ].

Surprisingly, more research papers mentioned the topic of human vs. artificial relationships as an ethical concern than autonomy. Autonomy is often the most discussed ethics topic when it comes to use of technology [ 96 ]. However, fears associated with technology replacing human care has recently gained traction [ 109 , 110 , 111 ].The significance of this theme is likely due to the fact that caregiving for older persons has been (and is) a very human-centric activity [ 112 ]. As mentioned before, the persons willing and able to do this labor (both paid and unpaid caregiver) are limited and their pool is shrinking [ 113 ]. The idea of technology possibly filling this gap is not new [ 114 ], but is also clearly causing wariness among both older persons and caregivers, as we have discovered [ 56 , 61 ]. Frequently mentioned was the fear of care being replaced by technology. This finding was to be expected, as nursing is not the only profession where introduction of technology caused fears of job loss [ 115 ]. Within this ethical concern, the importance of human touch and human interaction was underlined [ 110 , 111 ]. Human touch is an important asset for caregivers when they care for older patients, particularly those with dementia, as it is one of the few ways to establish connection and to calm the patient with dementia [ 116 ]. Similarly, human touch and face-to-face interactions are mentioned as a critical aspect of caregiving in general, both for the care recipient and the caregiver [ 117 , 118 ]. While caregivers see the aspect of touching and interacting with older care recipients as a way to make their actions more meaningful and healing [ 90 , 117 ], for care recipients being touched, talked and listened to is part of feeling respected and experiencing dignity [ 118 , 119 ]. Introducing technology into the caregiving profession may therefore quickly elicit associations with cold and lifeless objects [ 59 ]. Future developments, both in the design of the technologies themselves and their implementation in caregiving will require critical discussion among concerned stakeholders and careful decision on how and to what extent the human touch and human care must be preserved.

A unique ethical concern that we have not seen in previous research [ 120 , 121 ] is responsibility, and remarkable within this concern was SHHTs’ negative impact on it. As previously mentioned, the human being and human interaction are seen as central to caregiving [ 117 , 118 ]. This can possibly be extended to concepts exclusively attributable to humans, such as the concept of moral responsibility [ 122 ]. Shifting caregiving tasks onto a technological device, which, by being a device and not a human carer, cannot be morally responsible in the same way as a human being can [ 123 ], may introduce a sense of void that caregivers are reluctant to create. Studies have shown that a mismatch in professional and personal values in nursing causes emotional discomfort and stress [ 124 ], therefore the shift in the professional environment caused by SHHTs is likely to be met with aversion. Additionally, the negative impact of SHHTs on caregiving responsibility was also tied to practical concerns, like not having enough time to learn how to use the technology by the caregivers [ 35 ], or needing to have access to and checking the older person’s health data [ 36 ]. Such concerns point to the possibility that SHHTs can create unforeseen tasks, which could turn into true burdens, instead of alleviating caregivers. Indeed, there are indications that the increase in information about the older person through monitoring technologies causes stress for both caregivers and older persons, as the former feel pressure to look at the available data, while the latter prefer to hide unfavorable information to not seem burdensome for their caregivers [ 125 ]. Another consequence of SHHTs that emerged as a sub-category was the renegotiation of responsibilities among the different stakeholders. In the field of (assistive) technology, this renegotiation is an ongoing process with efforts to make technology and its developers more accountable, through new policies and regulations [ 126 ]. In the realm of assistive technology in healthcare, these negotiations focus on high-risk cases and emergencies [ 127 ]. Who is responsible for the death of a person if the assistive technology failed to recognize an emergency, or to alert humans in time? Such issues around responsibility and legal liability are partially responsible for the slow uptake of technology in caregiving [ 128 ].

Another important but less discussed ethical concern was ageism and stigma. Ageist prejudices include being perceived as slow, useless, burdensome, and incompetent [ 129 ]. Fear of aging and becoming a burden to others is a fear many older persons have, as current social norms demand independence until death [ 130 ]. Furthermore, the general ubiquitous use of technology has possibly exacerbated the issue of ageism, as life became fast paced and more pressure is placed on aging persons to keep up [ 131 ]. While this would call for more attention to studying ageism in relation to technology, our findings indicate that, it does not unfortunately seem at the forefront of concerns that are prevalent in the literature (and thereby the society).

Related to ageism, is the wish of older persons to not be perceived as old and/or in the need of assistance (in the form of technology) explains the prevalent demand for unobtrusive technology. Obtrusiveness, in the context of SHHTs, is defined as “undesirably prominent and or/noticeable”, yet this definition should include the user’s perception and environment, and is thus not an objectively applicable definition [ 132 ]. Nevertheless, we can infer that by “unobtrusive”, users mean SHHTs that is not noticeable by them or, mostly importantly, by other persons to possibly reduce stigma associated with using a technology deemed to be for persons with certain limitations. Further research will have to confirm if unobtrusive technology actually reduces stigma and/or fosters acceptance of such SHHTs in caregiving.

Lastly, the sub-theme of stigmatization of women and immigrants in caregiving and possibly exacerbating their caregiving burden through technology was only discovered in two theoretical publications [ 47 , 71 ]. While it is well known that caregiving burden mostly falls upon women [ 133 , 134 ], many of them with a migration background when it comes to live-in caregivers [ 135 , 136 ]. It is surprising that we found no redistribution of burden of care with technology. This is likely due to the fact that caregiving – be it technologically assisted or not – remains perceived as a more feminine and, unfortunately, low status profession [ 137 ]. The development of technology, however, are still mostly associated with masculinity This tension between the innovators and actual users of technology can lead to the exacerbation of stigma for female and migrant caregivers, as the human bias is conserved by the technology, instead of disrupted through it [ 137 ].

Finally, trust was an expected ethical concern, given that it is a widely discussed topic in relation to technology (see for example, [ 123 , 138 ] and also in the context of nursing [ 95 , 139 ]. Older persons were trusting caregivers to understand SHHTs [ 48 ], while caregivers feared that older persons would not trust the used technology, even though said persons did not express such concerns [ 32 ]. A possibility to mitigate such misunderstandings and put both caregivers and care recipients on an equal understanding of the technology are education tools [ 140 ]. Another surprising finding was that some older persons were inclined to trust SHHTs even more than human caregivers, as they were seen as more reliable [ 70 ]. This trust in technology was increased when a physical robot instead of an only virtual agent was involved [ 60 , 65 ]. Studies in the realm of embodiment of virtual agents and robots suggest that the presence of a body or face promotes human-like interactions with said agents [ 51 ]. Furthermore, our systematic review discovered other characteristics which promote trust in SHHTs, such as perceived usefulness [ 94 ] or time spent with the technology [ 59 ]. Another important aspect is the already existing trust in the person introducing the technology to the user [ 34 , 93 ]. In combining these characteristics in the design and implementation of SHHTs in caregiving, researchers and technology developers need to find creative mechanisms to facilitate trustworthiness and foster adoption of new technologies in caregiving.

Limitations

While we searched 10 databases for publications over a span of 20 years, we are aware that older or newer publications will have escaped our systematic review. Relevant new literature that we have found when writing our results have been incorporated in this manuscript. Furthermore, as we specifically refrained from using terms related to ethics in our search strings to also capture the instances of absence of ethical concerns, this choice may have led to missing a few articles as a consequence, especially in regards to theoretical publications. Lastly, due to lack of resources, we were unable to carry out independent data extraction for all included papers (N = 156) and chose to validate the quality of extracted data by using a random selection of 10% of the included sample. Since there was high agreement on extracted data, we are confident about the quality of our study findings.

SHHTs offer the possibility to mitigate the shortage of human caregiving resources and to enable older persons to age in place, being adequately supported by technology. However, this shift in caregiving comes with ethical challenges. If and how these ethical challenges are mentioned in the current research around SHHTs in caregiving for older persons was the goal of this systematic review. Through analyzing 156 articles, both empirical and theoretical, we discovered that, while over one third of articles did not mention any ethical concerns whatsoever, the other two thirds discussed a plethora of ethical issues. Specifically, we discovered the emergence of concerns with the use of technology in the care of older persons around the theme of human vs. artificial relationships, ageism and stigma, and responsibility. In short, our systematic review offers a comprehensive overview of the currently discussed ethical issues in the context of SHHTs in caregiving for older persons. However, scholars in the fields of gerontology, ethics, and technology working on such issues would be already (or should be) aware that ethical concerns will change with each developing technology and the population it is used for. For instance, with the rise of Artificial intelligence/Machine Learning, new intelligent or smart technologies will continue to mature with use and time. Thus, ethical value such as autonomy will require re-evaluation with this significant content development as well as deciding, if the person would/should be asked to re-consent or how should this decision making proceed should he or she have developed dementia. In sum, more critical work is necessary to prospectively act on ethical concerns that may arise with new and developing technologies that could be used in reducing caregiving burden now and in the future.

Data Availability

All data generated or analyzed during this systematic review are included in this published article and its appendices. Appendix part 1 contains all included articles and their characteristics. Appendix part 2 contains the search strategy and all search strings for all searched databases, as well as the PROSPERO registration number.

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Acknowledgements

We thank the information specialist of the University of Basel who advised us on our search strategy.

Open access funding provided by University of Basel. This study was supported financially by the Swiss National Science Foundation (SNF NRP-77 Digital Transformation, Grant Number 407740_187464/1) as part of the SmaRt homES, Older adUlts, and caRegivers: Facilitating social aCceptance and negotiating rEsponsibilities [RESOURCE] project. The funder neither took part in the writing process, nor does any part of the views expressed in the review belong to the funder.

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Creation of the search strategy and data extraction was a joint effort of NAF and AT. FP and TW extracted data and prepared it for analysis. AT contributed majorly to the data analysis, together with NAF who is the first author of this manuscript. TW and BE provided final comments and edits. All authors read and approved the manuscript before submission.

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Correspondence to Nadine Andrea Felber .

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Felber, N.A., Tian, Y., Pageau, F. et al. Mapping ethical issues in the use of smart home health technologies to care for older persons: a systematic review. BMC Med Ethics 24 , 24 (2023). https://doi.org/10.1186/s12910-023-00898-w

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Received : 15 September 2022

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Published : 29 March 2023

DOI : https://doi.org/10.1186/s12910-023-00898-w

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