Sex Differences in Pain Scores and Localization in Inflammatory Arthritis: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: To systematically identify and examine reports of sex-stratified pain measurements in patients with inflammatory arthritis. METHODS: Data sources included PubMed (1950 to April 2010), Embase (1980 to April 2010), and manual searches of reference lists and conference abstracts. We included cohort studies and randomized trials comparing pain scores, treatment efficacy at reducing pain, or pain localization, between females and males with inflammatory arthritis [rheumatoid arthritis (RA), ankylosing spondylitis, psoriatic arthritis, and reactive arthritis]. RESULTS: Twenty-six cohorts and 1 randomized trial reported sex-stratified pain scores, and all but 1 cohort identified worse pain scores at enrollment in females. In a metaanalysis of mean visual analog scale (VAS) scores (0 to 10) in 16 RA cohort studies (reporting on 21,612 females and 6871 males), the standardized mean difference in VAS was 0.21 (95% CI 0.16, 0.26). Treatment with disease-modifying therapy results in improvement in mean scores for both sexes; however, female absolute scores remain higher. In 12 spondyloarthropathy cohorts reporting pain localization, females develop more peripheral arthritis during their disease course (68.9% vs 51.2%) but less inflammatory back pain (50.6% vs 66.4%). CONCLUSION: We identified important sex differences in pain scores in inflammatory arthritis, with higher pain levels in females. In spondyloarthritis, females develop more peripheral arthritis and have less frequent spinal involvement compared to males. These differences may affect a clinician's perception of disease severity and activity, and thus influence management decisions.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".