Unmasking the Adverse Impacts of Sex Bias on Science and Research Animal Welfare
Bibliographic record
Abstract
Sex bias in biomedical and natural science research has been prevalent for decades. In many cases, the female estrous cycle was thought to be too complex an issue to model for, and it was thought to be simpler to only use males in studies. At times, particularly when studying efficacy and safety of new therapeutics, this sex bias has resulted in over- and under-medication with associated deleterious side effects in women. Many sex differences have been recognized that are unrelated to hormonal variation occurring during the estrous cycle. Sex bias also creates animal welfare challenges related to animal over-production and wastage, insufficient consideration of welfare (and scientific) impact related to differential housing of male vs female animals within research facilities, and a lack of understanding regarding differential requirements for pain recognition and alleviation in male versus female animals. Although many funding and government agencies require both sexes to be studied in biomedical research, many disparities remain in practice. This requires further enforcement of expectations by the Institutional Animal Care and Use Committee when reviewing protocols, research groups when writing grants, planning studies, and conducting research, and scientific journals and reviewers to ensure that sex bias policies are enforced.
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 | 0.088 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".