Sex Bias Is Increasingly Prevalent in Preclinical Cardiovascular Research: Implications for Translational Medicine and Health Equity for Women
Bibliographic record
Abstract
nsuring that women are adequately represented in clinical trials is recognized as essential for sex equity in health. However, the use of female animal models and sex-based reporting have not been equally enforced in preclinical stages of research, which often precede and inform clinical trials. In 2014, the National Institutes of Health announced that it would require that sex be considered as a biological variable in applications for preclinical research funding, 1 yet a reluctance to include female animal models in preclinical experiments persists. Inappropriately inferring experimental findings to both sexes when a single sex is studied or when sex is not specified has the potential to disadvantage women by skewing our understanding of disease processes toward male-predominant patterns and by reducing the likelihood of female-specific therapeutics advancing to the clinical realm. We therefore systematically examined all preclinical cardiovascular studies published in American Heart Association journals with archives spanning at least 10 years (Circulation, Circulation Research, Hypertension, Stroke, and Arteriosclerosis, Thrombosis, and Vascular Biology [ATVB]) for evidence of sex bias.
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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".