Female sex-specific considerations to improve rigor and reproducibility in cardiovascular research
Bibliographic record
Abstract
Cardiovascular disease is the leading cause of death in women. Despite recognition of sex-specific differences in cardiovascular health, females are underrepresented across all aspects of cardiovascular research, playing a key role in reducing rigor and reproducibility in cardiovascular research and contributing to these poorer health outcomes. Therefore, we propose a framework to capture factors associated with the female sex at the preclinical, recruitment, data collection, and data analysis stages. In preclinical cardiovascular research, female experimental models are commonly excluded despite similar variability in findings compared with males. To reduce this sex bias, the inclusion of female models and the incorporation of sex as a biological variable are critical to improve reproducibility and inform clinical research and care. Although funding agencies have mandated the inclusion of women in clinical trials, greater efforts are needed to achieve optimal participation-to-prevalence ratio to increase the generalizability of results to real-world settings. Female participants face more stringent exclusion criteria in research compared with males owing to sex-specific factors. However, their routine exclusion from cardiovascular research is not only unethical but limits generalizability and applicability to clinical practice. Identifying sex assigned at birth, collecting information on female sex-specific and -predominant factors associated with cardiovascular health and risk, and stratifying data by sex, including adverse events, are essential to ensure reproducibility and relevance of findings to target populations. Increasing female representation and the incorporation of female sex-specific cardiovascular risk factors in cardiovascular research will not only lead to enhanced rigor and reproducibility but improved cardiovascular health for all.
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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 | Metaresearch Domain: Reproducibility · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Reproducibility · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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, 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".