Temporal Trends and Clinical Trial Characteristics Associated With the Inclusion of Women in Heart Failure Trial Steering Committees: A Systematic Review
Bibliographic record
Abstract
Background: Trial steering committees (TSCs) steer the conduct of randomized controlled trials (RCTs). We examined the gender composition of TSCs in impactful heart failure RCTs and explored whether trial leadership by a woman was independently associated with the inclusion of women in TSCs. Methods: We systematically searched MEDLINE, EMBASE, and CINAHL for heart failure RCTs published in journals with impact factor ≥10 between January 2000 and May 2019. We used the Jonckheere-Terpstra test to assess temporal trends and multivariable logistic regression to explore trial characteristics associated with TSC inclusion of women. Results: Of 403 RCTs that met inclusion criteria, 127 (31.5%) reported having a TSC but 20 of these (15.7%) did not identify members. Among 107 TSCs that listed members, 56 (52.3%) included women and 6 of these (10.7%) restricted women members to the RCT leaders. Of 1213 TSC members, 11.1% (95% CI, 9.4%–13.0%) were women, with no change in temporal trends ( P =0.55). Women had greater odds of TSC inclusion in RCTs led by women (adjusted odds ratio, 2.48 [95% CI, 1.05–8.72], P =0.042); this association was nonsignificant when analysis excluded TSCs that restricted women to the RCT leaders (adjusted odds ratio 1.46 [95% CI, 0.43–4.91], P =0.36). Conclusions: Women were included in 52.3% of TSCs and represented 11.1% of TSC members in 107 heart failure RCTs, with no change in trends since 2000. RCTs led by women had higher adjusted odds of including women in TSCs, partly due to the self-inclusion of RCT leaders in TSCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".