Protection by inclusion: Increasing enrollment of women in cardiovascular trials
Bibliographic record
Abstract
Despite differences in biology that influence disease incidence, drug metabolism, and response to therapies, women remain under-enrolled in cardiovascular clinical trials. Estimates regarding treatment efficacy and safety are derived from male-predominant trial populations, with inadequate balance between sex subgroups for meaningful analysis on sex-specific treatment effects. Treatment strategies for women, particularly women of childbearing years, are derived from trials with predominantly men participants, from lower quality, observational studies, or anecdotal evidence. Guideline recommendations for women who are pregnant or lactating are typically based on opinion as there is little evidence to guide them. In this review, we discuss trial design factors independently associated with the under-enrollment of women, identify possible strategies to increase the enrollment of women in trials, and suggest multi-level actions that could close sex-based research disparities. Recruiting and retaining women trialists, independently associated with increased enrollment of women and Black, Indigenous, and Persons of Color (BIPOC) participants, and diversifying research teams may be effective approaches. Modifying trial design by eliminating default sex-specific exclusion criteria, developing patient-centered consent and participation processes, incorporating pragmatic follow-up schemes, and incorporating sex/gender analysis into trial planning may also increase the enrollment of women participants. Journals and funding bodies should require trials to report participant to prevalence ratios, sex-disaggregated trial flow, and sex-treatment interactions. Healthcare systems can help create research-ready cultures that both enhance patient engagement in trials and expedite end-of-trial knowledge translation.
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 | 0.206 | 0.388 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".