Representation of Women in Randomized Trials in Cardiac Surgery: A Meta‐Analysis
Bibliographic record
Abstract
Background Women have traditionally been underrepresented in randomized clinical trials (RCTs). We performed a systematic evaluation of the inclusion of women in cardiac surgery RCTs published in the past 2 decades. Methods and Results MEDLINE, EMBASE, and the Cochrane Library were searched (2000 to July 2020) for RCTs written in English, comparing ≥2 adult cardiac surgical procedures. The percentage of women enrolled and its association with year of publication, sample size, mean age, funding source, geographic location, number of sites involved, and interventions tested were analyzed using a meta‐analytic approach. Fifty‐one trials were included. Of 25 425 total patients, 5029 were women (20.8%; 95% CI, 17.6–24.4; range, 0.5%–57.9%). The proportion of women dropped significantly during the study period (29.6% in 2000 versus 13.1% in 2019, P <0.001). Women were significantly more represented in European trials (26.2%; 95% CI, 21.2–31.9), and less represented in trials of coronary bypass surgery versus other interventions (16.8%; 95% CI, 12.3–22.7 versus 33.6%; 95% CI, 27.4–40.5; P =0.0002) and in trials enrolling younger patients ( P =0.009); the percentage of women was higher in industry‐sponsored versus non‐industry sponsored trials (31.7%; 95% CI, 27.2–36.6 versus 15.5%; 95% CI, 10.0–23.2; P =0.0004) and was not associated with trial sample size ( P =0.52) or study design (multicenter versus monocenter: P =0.22). After exclusion of trials conducted at Veteran Affairs centers, women representation was 24.4% (95% CI, 21.1–28.0; range, 10.4%–57.9%), with no significant changes during the study period. Conclusions The proportion of women in cardiac surgery trials is low and likely inadequate to provide meaningful estimates of the treatment effect.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
| gpt | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.128 | 0.237 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.046 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".