Evidence map›Paper›PMID 35562851›Full record

ReviewAnnual review of biomedical data science2022

Best Practices on Big Data Analytics to Address Sex-Specific Biases in Our Understanding of the Etiology, Diagnosis, and Prognosis of Diseases.

Su Golder, Karen O'Connor, Yunwen Wang, Robin Stevens, Graciela Gonzalez-Hernandez

Abstract readReview
In one paragraph

Review in Annual review of biomedical data science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
  2. Influence of biological sex in inflammatory bowel diseases.Nature reviews. Gastroenterology & hepatology · 2025
    Review
  3. Health Equity in Clinical Research Informatics.Yearbook of medical informatics · 2023
    Review
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

5 authors.

Su GolderDepartment of Health Sciences, University of York, York, United Kingdom.
Karen O'ConnorDepartment of Biostatistics, Epidemiology and Informatics (DBEI), Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA; email: gragon@pennmedicine.upenn.edu.
Yunwen WangAnnenberg School for Communication and Journalism, University of Southern California, Los Angeles, California, USA.
Robin StevensAnnenberg School for Communication and Journalism, University of Southern California, Los Angeles, California, USA.
Graciela Gonzalez-HernandezDepartment of Biostatistics, Epidemiology and Informatics (DBEI), Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA; email: gragon@pennmedicine.upenn.edu.

Funding

Social Media Mining for PharmacovigilanceR01LM011176 · NLM · UNIVERSITY OF PENNSYLVANIA · PI GONZALEZ HERNANDEZ, GRACIELA, SHEN, LI · 2012 to 2021
$4.5M
UTILIZING TWITTER TO IDENTIFY AND RECRUIT YOUNG BLACK MEN AT HIGH RISK OF HIV INFECTIONR21DA049572 · NIDA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI STEVENS, ROBIN · 2019 to 2020
$450k
NIDA NIH HHS R21 DA049572NLM NIH HHS R01 LM011176
6 · The paper itself

Abstract

A bias in health research to favor understanding diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature on machine learning or natural language processing (NLP) techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (

Indexed as

Artificial IntelligenceData ScienceBig DataFemaleHumansMachine LearningMaleNatural Language Processingbiasethicsgender disparitieshealth disparitiesmachine learningnatural language processing

Identifiers

PMID35562851
PMCPMC11524028

What OpenQuestion holds

Textmetadata
LicenceTDM
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.