ReviewPatterns (New York, N.Y.)2022
Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records.
Review in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Improving radiology information systems for inclusivity of transgender and gender-diverse patients: what are the problems and what are the solutions? A systematic review.Journal of medical radiation sciences · 2024Pooled it
- The Extent Of Gender/Sex Variables Included In Clinical Algorithms Across Medical Specialties.Health affairs (Project Hope) · 2026Article
- Including gender data in electronic health records to improve the care provided to transgender and non-binary patients in Quebec: A queer bioethics analysis.International journal of transgender health · 2026Article
- The Double Historicity of Sex and Gender.Archives of sexual behavior · 2025Article
- Synthetic data and health equity: accounting for racism and sexism in health care delivery.Health affairs scholar · 2025Article
- Patient care practices for LGBTQ+ individuals in clinical genetics: A scoping review.Journal of genetic counseling · 2025Article
- Can Open Science Advance Health Justice? Genomic Research Dissemination in the Evolving Data-Sharing Landscape.The Hastings Center report · 2024Article
- Prospective Validation of a Suicide Event Risk Model in Transgender Patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Will clinical standards not be part of the choir? Harmonization between the HL7 gender harmony project model and the NASEM measuring sex, gender identity, and sexual orientation report in the United States.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Two researchers share how their cross disciplinary collaboration enables work to guide the future of data science.Patterns (New York, N.Y.) · 2022Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
False assumptions that sex and gender are binary, static, and concordant are deeply embedded in the medical system. As machine learning researchers use medical data to build tools to solve novel problems, understanding how existing systems represent sex/gender incorrectly is necessary to avoid perpetuating harm. In this perspective, we identify and discuss three factors to consider when working with sex/gender in research: "sex/gender slippage," the frequent substitution of sex and sex-related terms for gender and vice versa; "sex confusion," the fact that any given sex variable holds many different potential meanings; and "sex obsession," the idea that the relevant variable for most inquiries related to sex/gender is sex assigned at birth. We then explore how these phenomena show up in medical machine learning research using electronic health records, with a specific focus on HIV risk prediction. Finally, we offer recommendations about how machine learning researchers can engage more carefully with questions of sex/gender.
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Registered trials
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.