Sex as a biological variable: a contemporary perspective
Bibliographic record
Abstract
BACKGROUND: Incorporating sex as a biological variable (SABV) in biomedical research is essential to enhancing the translational relevance of scientific findings and ensuring equitable healthcare for both sexes. Despite policy advancements, disparities persist in the integration of SABV across research domains, particularly in cardiovascular disease, where presentation and treatment responses vary by sex. METHODS: This review synthesizes current literature and policy frameworks, including the NIH SABV mandate, to evaluate progress in SABV implementation. It also examines the roles of key stakeholders-funding agencies, publishers, and the pharmaceutical industry-in promoting or hindering SABV integration in preclinical and clinical research. RESULTS: Analysis reveals variable adherence to SABV policies, with persistent gaps in both study design and reporting. Case studies in cardiovascular research illustrate the consequences of SABV neglect, such as misdiagnosis and suboptimal treatment strategies. Positive shifts are observed in areas with strong policy enforcement and targeted funding incentives. CONCLUSIONS: Effective integration of SABV is critical for scientific rigor and healthcare equity. Strategies such as mandatory researcher training, policy accountability measures, and increased sex-disaggregated data reporting are needed. Emphasizing SABV in clinical trial design and analysis will help foster a more inclusive research environment and improve health outcomes for all. TRIAL REGISTRATION: Not applicable.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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".