Efficacy and safety of glucosamine, diacerein, and NSAIDs in osteoarthritis knee: a systematic review and network meta-analysis
Bibliographic record
Abstract
To conduct a systematic review and network meta-analysis of randomized controlled trials (RCTs) with the aims of comparing relevant clinical outcomes (that is, visual analog scores (VAS), total and sub-Western Ontario and McMaster Universities Osteoarthritis index (WOMAC) scores, Lequesne algofunctional index, joint space width change, and adverse events) between diacerein, glucosamine, and placebo.Medline and Scopus databases were searched from inception to 29 August 2014, using PubMed and Scopus search engines and included RCTs or quasi-experimental designs comparing clinical outcomes between treatments. Data were extracted from original studies. A network meta-analysis was performed by applying weight regression for continuous outcomes and a mixed-effect Poisson regression for dichotomous outcomes.Thirty-one of 505 identified studies were eligible. Compared to placebo, glucosamine showed a significant improvement with unstandardized mean differences (UMD) in total WOMAC, pain WOMAC, function WOMAC, and Lequesne score of -2.49 (95% confidence interval (CI) -4.14, -0.83), -0.75 (95% CI: -1.18, -0.32), -4.78 (95% CI: -5.96, -3.59), and -1.03 (95% CI: -1.34, -0.72), respectively. Diacerein clinically improves visual analog scores, function WOMAC, and stiffness WOMAC with UMD values of -2.23 (95% CI: -2.82, -1.64), -6.64 (95% CI: -10.50, -2.78), and -0.68 (95% CI: -1.20, -0.16) when compared to placebo.The network meta-analysis suggests that diacerein and glucosamine are equally efficacious for symptom relief in knee OA, but that the former has more side effects.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".