Guidelines on the use of sex and gender in cardiovascular research
Bibliographic record
Abstract
In cardiovascular research, sex and gender have not typically been considered in research design and reporting until recently. This has resulted in clinical research findings from which not only all women, but also gender-diverse individuals have been excluded. The resulting dearth of data has led to a lack of sex- and gender-specific clinical guidelines and raises serious questions about evidence-based care. Basic research has also excluded considerations of sex. Including sex and/or gender as research variables not only has the potential to improve the health of society overall now, but it also provides a foundation of knowledge on which to build future advances. The goal of this guidelines article is to provide advice on best practices to include sex and gender considerations in study design, as well as data collection, analysis, and interpretation to optimally establish rigor and reproducibility needed to inform clinical decision-making and improve outcomes. In cardiovascular physiology, incorporating sex and gender is a necessary component when optimally designing and executing research plans. The guidelines serve as the first guidance on how to include sex and gender in cardiovascular research. We provide here a beginning path toward achieving this goal and improve the ability of the research community to interpret results through a sex and gender lens to enable comparison across studies and laboratories, resulting in better health for all.
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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 | 0.036 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".