Evidence map›Paper›PMID 20157491›Full record

ArticleDatabase : the journal of biological databases and curation2009

Understanding PubMed user search behavior through log analysis.

Rezarta Islamaj Dogan, G Craig Murray, Aurélie Névéol, Zhiyong Lu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Database : the journal of biological databases and curation, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
103citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

103 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Guideline
  4. Trial
  5. A Technical Note on Bibliometric Analysis by Biblioshiny and VOSviewer.The Indian journal of radiology & imaging · 2026
    Review
  6. Article
  7. Review
  8. Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?Proceedings of machine learning research · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. PubMed features to save your time.Journal of hospital librarianship · 2024
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

43 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Rezarta Islamaj DoganNational Center for Biotechnology Information, US National Library of Medicine, Bethesda, MD 20894, USA.
G Craig Murray
Aurélie Névéol
Zhiyong Lu

Funding

PubMed Query Log Analysis and Use in Access InhancementZIALM200812 · NLM · NATIONAL LIBRARY OF MEDICINE · PI WILBUR, WILLY · 2009 to 2015
$1.4M
6 · The paper itself

Abstract

This article reports on a detailed investigation of PubMed users' needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users' needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users' interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users' decisions. Analysis of characteristics such as these plays a critical role in identifying users' information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval.Database URL:http://www.ncbi.nlm.nih.gov/PubMed.

Identifiers

PMID20157491
PMCPMC2797455

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.