Temporal Trends of Enrollment by Sex and Race in Major Cardiovascular Randomized Clinical Trials
Bibliographic record
Abstract
Background Women and racialized minorities continue to be underrepresented in cardiovascular trial outcomes data, despite comprising a significant global burden of cardiovascular disease (CVD). This study evaluated the impact of trial characteristics on the temporal enrollment of women and racialized minorities in prominent cardiovascular trials published between 1986-2023. Methods MEDLINE was searched for cardiovascular trials published in the Lancet , Journal of the American Medical Association, and the New England Journal of Medicine . Participant and investigator demographics, types of interventions, clinical indications, and funding sources were compared according to the enrollment of women or racialized minorities. Results From 799 studies, including 4,071,921 patients, the enrollment of women and racialized minorities significantly increased from 1986-2023 (both P = < 0.001). Although the enrollment of women varied by trial indication, comprising 25.0% of coronary artery disease (CAD), 35.2% of non-coronary/vascular, 13.8% of heart failure (HF), 17.0% of arrhythmia and 28.7% of other cardiovascular trials ( P = < 0.001), it did not differ by peer-reviewed vs industry funding. First authors who were women were more likely to enroll significantly more women than first authors who were men ( P = 0.01). Racialized enrollment increased significantly from 1986-2023 ( P = < 0.001). Conclusions Active efforts to increase diverse enrolment, along with improved reporting, including sex and race, in future cardiovascular trials may increase the generalizability of their findings and applicability to global populations.
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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 | 0.061 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".