The effects of sex and gender attributes on clinical outcomes: a systematic review
Bibliographic record
Abstract
BACKGROUND: Biological sex and sociocultural gender may influence changes in health status critical to clinical decision-making, yet scientific evidence of their effects on clinically relevant outcomes remain uncertain. We aimed to systematically review research on sex and gender effects on clinical outcomes and to assess the consistency and significance of associations between sex, gender, and clinical outcomes. METHODS: We searched Medline, Embase, PsycInfo, CINAHL, and Web of Science from each database's inception to November 20, 2023, and included English language peer-reviewed research utilizing standardized measures of sex and gender attributes in adults to measure their association with clinically relevant outcomes. We performed a risk of bias assessment and certainty assessment using criteria set a priori. We created visualizations of results with links to study quality and sex and gender attributes, which facilitated certainty assessment. We reported results across sex and gender-related attributes and measures. RESULTS: Of the 12,964 unique records identified, 19 studies with a total of 643,093 participants (54% male) were included in data synthesis. Four studies measured attributes of sex (testosterone, sex-specific polygenic score), and 15 studies measured attributes of gender (gender identity, roles, and adherence to masculine norms). We observed great heterogeneity in the direction and significance of the associations, resulting in evidence of moderate certainty only for the association between testosterone level and depression, and erectile function. We regarded all other evidence as very low in certainty. CONCLUSION: Research findings regarding the effects of sex and gender attributes on clinical outcomes is variable. However, results suggest that neither sex nor gender attributes should be ignored when investigating clinically relevant outcomes. To enhance certainty, future research should delve into sex and gender attributes concurrently, taking into account that clinical disorders are not evenly distributed across sexes and genders. This approach would provide needed evidence to drive precision medicine and person-centered care. PROSPERO: CRD42023456917. FUNDING: Global Brain Health Institute, Alzheimer's Association, and Alzheimer's Society UK Pilot Award for Global Brain Health Leaders (GBHI ALZ UK-23-971123); Canada Research Chairs Program for Neurological Disorders and Brain Health (CRC-2021-00074).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".