Comparative effectiveness of nonsurgical interventions in the treatment of patients with knee osteoarthritis
Bibliographic record
Abstract
Abstract Background: To find out, based on the available recent randomized controlled trials (RCTs), if the nonsurgical interventions commonly used for knee osteoarthritis patients are valid and quantify their efficiency. Methods: The database of MEDLINE and EMBASE were searched for RCTs evaluating nonsurgical treatment strategies on patients with mild to moderate knee osteoarthritis. A Bayesian random-effects network meta-analysis was performed. The primary outcome was the mean change from baseline in the Western Ontario and McMaster university (WOMAC) total score at 12 months. Raw mean differences with 95% credibility intervals were calculated. Treatments were ranked by probabilities of each treatment to be the best. Results: Thirteen trials assessed 7 strategies with WOMAC at 12 months: injection of platelet rich plasma (PRP), corticosteroids, mesenchymal stem cells (MSCs), hyaluronic acid, ozone, administration of nonsteroidal anti-inflammatory drugs with or without the association of physiotherapy. For treatment-specific effect size, a greater association with WOMAC decrease was found significantly for MSCs (mean difference, −28.0 [95% CrI, −32.9 to −22.4]) and PRP (mean difference, −19.9 [95% CrI, −24.1 to −15.8]). Rank probabilities among the treatments indicated that MSCs had a much higher probability (P = .91) of being the best treatment compared with other treatments, while PRP ranked as the second-best treatment (P = .89). Conclusion: In this systematic review and network meta-analysis, the outcomes of treatments using MSCs and PRP for the management of knee osteoarthritis were associated with long-term improvements in pain and function. More high quality RCTs would be needed to confirm the efficiency of MSCs and PRP for the treatment of patients with knee osteoarthritis.
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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 | 0.029 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".