Studying sex and gender differences in pain and analgesia: A consensus report
Bibliographic record
Abstract
In September 2006, members of the Sex, Gender and Pain Special Interest Group of the International Association for the Study of Pain met to discuss the following: (1) what is known about sex and gender differences in pain and analgesia; (2) what are the "best practice" guidelines for pain research with respect to sex and gender; and (3) what are the crucial questions to address in the near future? The resulting consensus presented herein includes input from basic science, clinical and psychosocial pain researchers, as well as from recognized experts in sexual differentiation and reproductive endocrinology. We intend this document to serve as a utilitarian and thought-provoking guide for future research on sex and gender differences in pain and analgesia, both for those currently working in this field as well as those still wondering, "Do I really need to study females?"
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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 | 0.227 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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".