Reporting of Sex Effects by Systematic Reviews on Interventions for Depression, Diabetes, and Chronic Pain
Bibliographic record
Abstract
Systematic reviews (SRs) have the potential to contribute uniquely to the evaluation of sex and gender differences (termed "sex effects"). This article describes the reporting of sex effects by SRs on interventions for depression, type 2 diabetes mellitus, and chronic pain conditions (chronic low back pain, knee osteoarthritis, and fibromyalgia). It includes SRs published since 1 October 2009 that evaluate medications, behavioral interventions, exercise, quality improvement, and some condition-specific treatments. The reporting of sex effects by primary randomized, controlled trials is also examined. Of 313 eligible SRs (86 for depression, 159 for type 2 diabetes mellitus, and 68 for chronic pain), few (n = 29) reported sex effects. Most SRs reporting sex effects used metaregression, whereas 9 SRs used subgroup analysis or individual-patient data meta-analysis. The proportion of SRs reporting the sex distribution of primary studies varied from a low of 31% (n = 8) for low back pain to a high of 68% (n = 23) for fibromyalgia. Primary randomized, controlled trials also infrequently reported sex effects, and most lacked an adequate sample size to examine them. Therefore, all SRs should report the proportion of women enrolled in primary studies and evaluate sex effects using appropriate methods whenever power is adequate.
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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 | 0.276 | 0.708 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.020 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".