Cardiovascular Health - Why We Need An Intersectional Sex and Gender Approach
Bibliographic record
Abstract
A sex and gender perspective in research involves an appreciation for the intersectionality between sex, gender, and other social factors (i.e. sexuality, socioeconomic status, race/ethnicity, etc.) with the risk and development of disease. This piece argues for the greater adoption of a sex and gender perspective in cardiovascular (CV) research. The absence of a sex and gender perspective has led to an underrepresentation of women and LGBTQ+ populations in studies and an underappreciation for both the biological and psychosocial impacts of sex and gender on pathogenesis.1,2 As a result of this insufficient understanding, these populations have faced a greater disease burden, poorer outcomes, and inequitable health interventions.3 The incorporation of a sex and gender lens in CV research will serve to lessen the burden of disease on these underserved populations through developing a greater understanding of the unique differences in the risk and progression of disease. Accordingly, this opinion piece hopes to illustrate the need for a sex and gender perspective in CV research in order to urge researchers, journal publishers, and supporting bodies to include sex and gender as a priority in future research.
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 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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".