Comparative effectiveness of oral pharmacologic interventions for knee osteoarthritis: A network meta-analysis
Bibliographic record
Abstract
Objectives: To explore the relative efficacy of oral pharmacologic interventions in the treatment of knee OA. Methods: A systematic literature review was conducted using the MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials databases to identify trials conducted in patients with knee OA with a minimum 6 weeks of follow-up. The standardized mean differences of the change from baseline to week 6 in Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain between the treatment groups were estimated using Bayesian random-effects network meta-analyses. Subgroup analyses of baseline pain status (high, pain score ≥60 mm; low, pain score <60 mm) were performed. Results: Of 4067 manuscripts, 44 were included in the evidence synthesis. Etoricoxib had the highest ranking for improving WOMAC pain (probability of being top ranked, p (best) = .43) followed by naproxen (p (best) = .12), acetaminophen (AAP) (p (best) = .04), and celecoxib (p (best) = .02). The top three ranked interventions were etoricoxib, celecoxib and aceclofenac in the higher pain group, and tramadol, celecoxib, and diclofenac in the lower pain group. Conclusion: In the overall analysis, etoricoxib, celecoxib, and aceclofenac had the highest rankings for improving WOMAC pain. The ability to improve knee OA symptoms may differ depending on baseline pain and radiologic features.
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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 | 0.030 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.048 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".