Evaluating sex as a biological variable in preclinical research: the devil in the details
Bibliographic record
Abstract
Translating policy into action is a complex task, with much debate surrounding the process whereby US and Canadian health funding agencies intend to integrate sex and gender science as an integral component of methodological rigor and reporting in health research. Effective January 25, 2016, the US National Institutes of Health implemented a policy that expects scientists to account for the possible role of sex as a biological variable (SABV) in vertebrate animal and human studies. Applicants for NIH-funded research and career development awards will be asked to explain how they plan to factor consideration of SABV into their research design, analysis, and reporting; strong justification will be required for proposing single-sex studies. The Canadian Institutes of Health Research is revising their peer review accreditation process to ensure that peer reviewers are skilled in applying a critical lens to protocols that should be incorporating sex and gender science. The current paper outlines the components that peer reviewers in North America will be asked to assess when considering whether SABV is appropriately integrated into research designs, analyses, and reporting. Consensus argues against narrowly defining rules of engagement in applying SABV, with criteria provided for reviewers as guidance only. Scores will not be given for each criterion; applications will be judged on the overall merit of scientific innovation, rigor, reproducibility, and potential impact.
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 | 0.759 | 0.840 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.012 | 0.061 |
| Scholarly communication | 0.032 | 0.043 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.027 | 0.029 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".