Sex in basic research: concepts in the cardiovascular field
Bibliographic record
Abstract
Women and men, female and male animals and cells are biologically different, and acknowledgement of this fact is critical to advancing medicine. However, incorporating concepts of sex-specific analysis in basic research is largely neglected, introducing bias into translational findings, clinical concepts and drug development. Research funding agencies recently approached these issues but implementation of policy changes in the scientific community is still limited, probably due to deficits in concepts, knowledge and proper methodology. This expert review is based on the EUGenMed project (www.eugenmed.eu) developing a roadmap for implementing sex and gender in biomedical and health research. For sake of clarity and conciseness, examples are mainly taken from the cardiovascular field that may serve as a paradigm for others, since a significant amount of knowledge how sex and oestrogen determine the manifestation of many cardiovascular diseases (CVD) has been accumulated. As main concepts for implementation of sex in basic research, the study of primary cell and animals of both sexes, the study of the influence of genetic vs. hormonal factors and the analysis of sex chromosomes and sex specific statistics in genome wide association studies (GWAS) are discussed. The review also discusses methodological issues, and analyses strength, weaknesses, opportunities and threats in implementing sex-sensitive aspects into basic research.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".