Trial characteristics associated with under‐enrolment of females in randomized controlled trials of heart failure with reduced ejection fraction: a systematic review
Bibliographic record
Abstract
AIMS: To evaluate temporal trends in the enrolment of females in randomized controlled trials (RCTs) of heart failure with reduced ejection fraction (HFrEF) published in high-impact journals, and assess RCT characteristics associated with under-enrolment. METHODS AND RESULTS: We searched MEDLINE, EMBASE and CINAHL for studies published from January 2000 to May 2019 in journals with impact factor ≥10. We included RCTs that recruited adults with HFrEF. We used a 20% threshold below the sex distribution of HFrEF to define under-enrolment. We used multivariable logistic regression to assess trial characteristics independently associated with under-enrolment. We included 317 RCTs. Among the 183 097 participants, mean (standard deviation) age was 63.0 (7.0) years and 25.5% were female. Females were under-enrolled in 71.6% [95% confidence interval (CI) 66.6-76.6%] of the RCTs; enrolment did not increase significantly between 2000-2019. Sex-related eligibility criteria [odds ratio (OR) 2.05, 95% CI 1.01-4.16; P = 0.046]; recruitment in ambulatory settings (OR 2.56, 95% CI 1.37-4.81; P = 0.003); trial coordination in North America (OR 4.44, 95% CI 1.09-18.07; P = 0.037), Europe (OR 6.79, 95% CI 1.63-27.39; P = 0.018) and Asia (OR 9.33, 95% CI 1.40-12.40; P = 0.033); drug (OR 1.76, 95% CI 1.96-7.36; P < 0.001) and device/surgical interventions (OR 1.69, 95% CI 1.16-9.43; P = 0.002); and men in first and last authorship position (OR 1.32, 95% CI 1.12-3.54; P = 0.047) were associated with under-enrolment of females. CONCLUSIONS: Females were under-enrolled relative to disease distribution in a majority of high-impact HFrEF RCTs, with no change in temporal trends between 2000 and 2019. Trial characteristics and gender of trial leaders were associated with under-enrolment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".