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.
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
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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 | 0.002 |
| 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; a candidate call from one teacher head, 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".