Evidence map›Paper›PMID 27102014›Full record

ArticleScientific reports2016

Systematic Analysis of Adverse Event Reports for Sex Differences in Adverse Drug Events.

Yue Yu, Jun Chen, Dingcheng Li, Liwei Wang, Wei Wang, Hongfang Liu

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.

0numbers the graph read from it
0cells of the map it votes in
79citing papers in PubMed
9.6field-weighted citation impact, top 2% of its field
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

79 citing papers in PubMed, 174 citations in OpenAlex.

  1. Evaluating the Safety, Tolerability, and Disposition of Trazpiroben, a DClinical pharmacology in drug development · 2022
    Trial
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Observational
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Metabolic Messengers: oestradiol.Nature metabolism · 2025
    Review
  19. Article
  20. Article

19 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

6 authors at 3 institutions in 2 countries.

Yue YuDepartment of Medical Informatics, School of Public Health, Jilin University, Changchun, Jilin 130021, China.
Jun ChenDepartment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55901, USA.
Dingcheng LiDepartment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55901, USA.
Liwei WangDepartment of Medical Informatics, School of Public Health, Jilin University, Changchun, Jilin 130021, China.
Wei WangDepartment of Medical Informatics, School of Public Health, Jilin University, Changchun, Jilin 130021, China.
Hongfang LiuDepartment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55901, USA.
Mayo Clinic in Florida · USJilin Medical University · CNJilin University · CN

Funding

Methods for generalized ontology terms enrichment analysisR01LM011369 · NLM · STANFORD UNIVERSITY · PI SHAH, NIGAM H · 2013 to 2020
$5.0M
Natural language processing for clinical and translational researchR01GM102282 · NIGMS · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG, PAKHOMOV, SERGUEI VS · 2013 to 2016
$2.5M
NIGMS NIH HHS R01 GM102282NLM NIH HHS R01 LM011369
6 · The paper itself

Abstract

Increasing evidence has shown that sex differences exist in Adverse Drug Events (ADEs). Identifying those sex differences in ADEs could reduce the experience of ADEs for patients and could be conducive to the development of personalized medicine. In this study, we analyzed a normalized US Food and Drug Administration Adverse Event Reporting System (FAERS). Chi-squared test was conducted to discover which treatment regimens or drugs had sex differences in adverse events. Moreover, reporting odds ratio (ROR) and P value were calculated to quantify the signals of sex differences for specific drug-event combinations. Logistic regression was applied to remove the confounding effect from the baseline sex difference of the events. We detected among 668 drugs of the most frequent 20 treatment regimens in the United States, 307 drugs have sex differences in ADEs. In addition, we identified 736 unique drug-event combinations with significant sex differences. After removing the confounding effect from the baseline sex difference of the events, there are 266 combinations remained. Drug labels or previous studies verified some of them while others warrant further investigation.

Indexed as

Drug-Related Side Effects and Adverse ReactionsHumansSex FactorsUnited States

Identifiers

PMID27102014
PMCPMC4840306
OpenAlexW2336561662

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.