Evaluating sex as a biological variable in preclinical research: the devil in the details
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it