Participation of women in cardiovascular trials from 2017 to 2023: a systematic review
Bibliographic record
Abstract
Abstract Background Cardiovascular disease (CVD) is the leading cause of death globally for both men and women, yet women remain historically underrepresented in cardiovascular clinical trials (CV trials), despite facing a disproportionately high burden of morbidity and mortality in many forms of CVD. Purpose We aim to determine the representation of women across a broad range of CV trials. Methods The participation of women in CV trials registered on ClinicalTrials.gov from 2017 to 2023 was systematically determined through the extraction of publicly available information. Data was extracted to identify the country of study, disease type, trial size, clinical intervention and age of the participants. We calculated the proportion of women and the ratio of number of females to male participants (F:M ratio) for each trial. The female participation to prevalence ratio (PPR) was estimated for each trial based on the relative prevalence of the disease by sex in the specified region. Results We identified 1,079 registered CV trials, which included 1,396,104 participants, of whom 571,641 (41.0%) were women. The median F:M ratio was significantly lower for studies on arrhythmia (0.5), coronary heart disease (CHD) (0.39), acute coronary syndrome (ACS) (0.32) and heart failure (HF) (0.51), but was higher for obesity (1.44) and pulmonary hypertension (PH) (2.86) CV trials. F:M ratio was higher for trials on lifestyle intervention (1.51) than for drug trials. PPRs were low for clinical trials on CHD (0.66), ACS (0.79), and stroke (0.74). There was a trend to increasing PPR from 2017 to 2023, p=0.056. Conclusion Representation of women in CV trials varies by disease state, region, intervention, and sponsor type. Better understanding of reasons for these differences may enable more tailored interventions to improve representation of women globally and across therapeutic areas.Figure 1 Figure 2
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 | 0.042 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".