Evidence map›Paper›PMID 41476311›Full record

ReviewBiology of sex differences2025

When algorithms infer gender: revisiting computational phenotyping with electronic health records data.

Jessica Gronsbell, Hilary Thurston, Lillian Dong, Vanessa Ferguson, Diksha Sen Chaudhury, Braden O'Neill, Katrina S Sha, Rebecca Bonneville

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biology of sex differences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Jessica GronsbellDepartment of Statistical Sciences, University of Toronto, Toronto, M5G 1X6, ON, Canada. j.gronsbell@utoronto.ca.
Hilary ThurstonDepartment of Gender, Feminist, and Women's Studies, York University, Toronto, M3J 1P3, ON, Canada.
Lillian DongDepartment of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, M5S 1A8, ON, Canada.
Vanessa FergusonSchool of Health Policy & Management, York University, Toronto, M3N 3A7, ON, Canada.
Diksha Sen ChaudhuryDepartment of Statistical Sciences, University of Toronto, Toronto, M5G 1X6, ON, Canada.
Braden O'NeillDepartment of Family Medicine, University of British Columbia, V6T 1Z3, Vancouver, BC, Canada.
Katrina S ShaDepartment of Statistical Sciences, University of Toronto, Toronto, M5G 1X6, ON, Canada.
Rebecca BonnevilleResilient Minds Psychiatry, Ojai, 93023, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational phenotyping has emerged as a practical solution to the incomplete collection of data on gender in electronic health records (EHRs). This approach relies on algorithms to infer a patient's gender using the available data in their health record, such as diagnosis codes, medication histories, and information in clinical notes. Although intended to improve the visibility of trans and gender-expansive populations in EHR-based biomedical research, computational phenotyping raises significant methodological and ethical concerns related to the potential misuse of algorithm outputs. In this paper, we provide a narrative review of computational phenotyping of gender and examine its challenges through a critical lens. We also highlight existing recommendations for biomedical researchers and propose priorities for future work in this domain.

Indexed as

AlgorithmsComputational BiologyElectronic Health RecordsPhenotypeSex CharacteristicsFemaleHumansMaleBiasComputational phenotypingElectronic health recordsEthicsGenderTransgender persons

Identifiers

PMID41476311
PMCPMC12865949

What Socratic 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 Socratic graph.