Abstract 13205: Underrepresentation of Women as Clinical Trialists in Heart Failure: A Systematic Review of Randomized Controlled Trials Published in High-impact Medical Journals
Bibliographic record
Abstract
Introduction: Clinical trials change practice in cardiology but leading them typically requires advanced research training, mentorship, sponsorship, and networking. Women are underrepresented in cardiology and report challenges in obtaining mentorship and networking opportunities. Objective: To evaluate the gender distribution of first and senior authors in practice-changing clinical trials in heart failure (HF), and explore clinical trial characteristics associated with women as first authors. Methods: We searched MEDLINE, EMBASE and CINAHL databases for randomized controlled trials (RCTs) published January 2000 - May 2019. We included RCTs that recruited adults with HF with reduced ejection fraction that were published in medical journals with an impact factor of > 10. Two reviewers screened and extracted data independently. We determined the gender distribution of the authors, assessed temporal trends in authorship, and used logistic regression to identify characteristics associated with women in first authorship positions. Results: The search identified 10,596 unique studies, of which 317 met eligibility criteria. We found that 15.8%, 11.7%, and 10.7% of first, senior, and corresponding authors, respectively, were women. Multivariable analysis revealed that the first author was more likely to be a woman if the senior author of a clinical trial was a woman (odds ratio [OR] 2.17, 95% confidence interval [CI] 1.04-4.66, p=0.039), and if the trial was conducted at a single rather than multiple centers (OR 1.40, 95% CI 1.09-6.48, p=0.035). Source of funding (public or industry), type of intervention (health service, drug, device, or surgery), and region of trial coordination (Europe, North America, Central and South America, Asia, and Australia) were not associated with gender of first authors. The proportion of trials that included women as authors has decreased from 2000 to present. Conclusion: Women are under-represented as clinical trialists in HF. Adjusting for other trial characteristics, the first author of a clinical trial was more likely to be a woman if the senior author was a woman. Mentoring women as clinical trialists in the present era may be a strategic way to increase the gender diversity of clinical trialists in years to come.
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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 | 0.089 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".