Sex and gender-based analysis and diversity metric reporting in acute care trials published in high-impact journals: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To characterise sex and gender-based analysis (SGBA) and diversity metric reporting, representation of female/women participants in acute care trials and temporal changes in reporting before and after publication of the 2016 Sex and Gender Equity in Research guideline. DESIGN: Systematic review. DATA SOURCES: We searched MEDLINE for trials published in five leading medical journals in 2014, 2018 and 2020. STUDY SELECTION: Trials that enrolled acutely ill adults, compared two or more interventions and reported at least one clinical outcome. DATA ABSTRACTION AND SYNTHESIS: 4 reviewers screened citations and 22 reviewers abstracted data, in duplicate. We compared reporting differences between intensive care unit (ICU) and cardiology trials. RESULTS: We included 88 trials (75 (85.2%) ICU and 13 (14.8%) cardiology) (n=111 428; 38 140 (34.2%) females/women). Of 23 (26.1%) trials that reported an SGBA, most used a forest plot (22 (95.7%)), were prespecified (21 (91.3%)) and reported a sex-by-intervention interaction with a significance test (19 (82.6%)). Discordant sex and gender terminology were found between headings and subheadings within baseline characteristics tables (17/32 (53.1%)) and between baseline characteristics tables and SGBA (4/23 (17.4%)). Only 25 acute care trials (28.4%) reported race or ethnicity. Participants were predominantly white (78.8%) and male/men (65.8%). No trial reported gendered-social factors. SGBA reporting and female/women representation did not improve temporally. Compared with ICU trials, cardiology trials reported significantly more SGBA (15/75 (20%) vs 8/13 (61.5%) p=0.005). CONCLUSIONS: Acute care trials in leading medical journals infrequently included SGBA, female/women and non-white trial participants, reported race or ethnicity and never reported gender-related factors. Substantial opportunity exists to improve SGBA and diversity metric reporting and recruitment of female/women participants in acute care trials. PROSPERO REGISTRATION NUMBER: CRD42022282565.
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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 | 0.216 | 0.600 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".