Doubly blind: a systematic review of gender in randomised controlled trials
Bibliographic record
Abstract
BACKGROUND: Although observational data show social characteristics such as gender or socio-economic status to be strong predictors of health, their impact is seldom investigated in randomised controlled studies (RCTs). OBJECTIVE & DESIGN: Using a random sample of recent RCTs from high-impact journals, we examined how the most often recorded social characteristic, sex/gender, is considered in design, analysis, and interpretation. Of 712 RCTs published from September 2008 to 31 December 2013 in the Annals of Internal Medicine, British Medical Journal, Lancet, Canadian Medical Association Journal, or New England Journal of Medicine, we randomly selected 57 to analyse funding, methods, number of centres, documentation of social circumstances, inclusion/exclusion criteria, proportions of women/men, and reporting about sex/gender in analyses and discussion. RESULTS: Participants' sex was recorded in most studies (52/57). Thirty-nine percent included men and women approximately equally. Overrepresentation of men in 43% of studies without explicit exclusions for women suggested interference in selection processes. The minority of studies that did analyse sex/gender differences (22%) did not discuss or reflect upon these, or dismissed significant findings. Two studies reinforced traditional beliefs about women's roles, finding no impact of breastfeeding on infant health but nevertheless reporting possible benefits. Questionable methods such as changing protocols mid-study, having undefined exclusion criteria, allowing local researchers to remove participants from studies, and suggesting possible benefit where none was found were evident, particularly in industry-funded research. CONCLUSIONS: Social characteristics like sex/gender remain hidden from analyses and interpretation in RCTs, with loss of information and embedding of error all along the path from design to interpretation, and therefore, to uptake in clinical practice. Our results suggest that to broaden external validity, in particular, more refined trial designs and analyses that account for sex/gender and other social characteristics are needed.
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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 | 0.231 | 0.571 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.038 | 0.024 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".