THE EVOLUTION OF AGE AND WOMEN REPRESENTATION IN THE MOST CITED RANDOMIZED TRIALS OF CARDIOLOGY OF THE LAST 20 YEARS
Bibliographic record
Abstract
Older adults and women have historically been underrepresented in RCTs of cardiology. Recent temporal evolution and factors influencing representation are incompletely investigated. We aimed to contrast age and women representation in the most influential RCTs in cardiology of the last 20 years to population prevalence, and to assess the study factors affecting representation. We selected the 25 most cited cardiology articles each year between 1996 and 2015, and extracted mean age, percentage of women, funding source, sample size, disease condition, intervention type, and exclusion criteria. The outcomes were the evolution of the mean age and the percentage of women over time. We analyzed 500 studies, where the mean age was 62.6 ± 7.4 years and the median percentage of women was 28.6% (22.2–40.5). Compared to population prevalence derived from NHANES 2015–2016, gaps in representation were apparent, and more pronounced for CAD (−5.0 years, −27.2% women) and heart failure (−6.0 years, −25.4% women). Mean age (0.15 year/year, 95%CI 0.04 -0.26) and percentage of women (0.29%/year, 95%CI 0.09–0.48), slightly but significantly increased over time. Private funding, small sample size, and exclusions pertaining to maximal age, atrial fibrillation, and diabetes were associated with a decreased mean age in multivariable linear regressions. Age and life expectancy exclusions were associated with lower women percentage. While age and women representation increased over time, the modest trends are unlikely to resolve the persistently wide gaps with actual populational prevalence, especially for CAD and HF. Representation is modulated by the cardiovascular condition studied and some modifiable protocol elements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.064 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".