Sex differences in the relationship between individual systemic markers of inflammation and pain in knee osteoarthritis
Bibliographic record
Abstract
Objective: There are suggestions that the relationship between inflammation and pain in osteoarthritis (OA) may differ by sex, yet studies have been limited. We investigated whether the relationship between knee-specific OA pain and systemic inflammatory markers differs by sex. Design: 196 patients scheduled for knee arthroplasty for OA were included. Questionnaires were completed and blood samples drawn pre-surgery. Questionnaire data: knee pain (WOMAC), sex, age, height, weight, comorbidities, depressive symptoms, and symptomatic joint count. Systemic inflammatory markers (cytokines IL-6, IL-8, IL-10, IL-1β and TNF-α) were measured by multiplex ELISA. A series of regression models with interaction terms between sex and ln-transformed inflammatory markers were estimated with pain score as the outcome. The adjusted relationship between pain and inflammatory markers, by sex, were presented graphically. Results: Mean age was 64 years (range 43-89); females comprised 58.7% of the sample. In adjusted analyses, similar relationships between knee pain and lnIL-10 (negative: β = -1.28, 95%CI (-1.97, -0.58)) and lnTNF-α (positive: β = 0.92, 95%CI (0.11, 1.76)) were found for females and males. In contrast, relationships between knee pain and lnIL-1β, lnIL-6 and lnIL-8 differed in direction for females and males. Specifically, for lnIL-1β and lnIL-8 they were positive for males, negative for females. The opposite was found with lnIL-6, negative for males, positive for females. Conclusion: These findings provide some evidence of sex-specific relationships between individual inflammatory markers and knee OA pain. They expose a need for further exploration of sex-differences in this context, with potential future implications for treatment or drug development in OA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".