Reporting of sex and gender in clinical trials of opioids and rehabilitation in military and Veterans with chronic pain
Bibliographic record
Abstract
Introduction: This study assesses the extent to which published research on pain management in Veterans has considered sex and gender differences in the design and reporting of results. Methods: The study identified randomized clinical trials (RCTs) that included active duty military or Veterans with non-cancer pain who were treated with rehabilitation and/or opioid(s) interventions (vs. any control group) published from January 2000 to February 2021. Authors extracted data on the inclusion of sex/gender in design and reporting using the Sex and Gender Equity in Research guidelines (SAGER) and Sex and Gender Methods Review guidelines to assess sex and gender reporting in the included RCTs. Results: The study included 21 RCTs. Reporting of sex and/or gender according to SAGER guidelines indicated all 21 RCTs did not specify any sex and/or gender information in the title/abstract, introduction, methods, results, or discussion. The reporting of sex and/or gender according to Sex and Gender Methods Review guidelines indicated 21 of 21 RCTs did not analyze to identify sex and/or gender differences in the literature review, research question(s), study design, analysis, or interpretation sections. Discussion: Despite expectations of important sex or gender differences in the effects of opioid or rehabilitation interventions for chronic pain in active duty military personnel or Veterans, no identified RCTs published over a 21-year period adequately reported or analyzed data in accordance with current standards for considering sex and gender. Failure to consider sex and gender in design, analysis, and reporting limits advancement of understanding sex/gender differences in pain management through research.
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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 | 0.127 | 0.396 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".