Glucosamine sulphate in the treatment of knee osteoarthritis: cost-effectiveness comparison with paracetamol
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to explore the cost-effectiveness of glucosamine sulphate (GS) compared with paracetamol and placebo (PBO) in the treatment of knee osteoarthritis. For this purpose, a 6-month time horizon and a health care perspective was used. MATERIAL AND METHODS: The cost and effectiveness data were derived from Western Ontario and McMaster Universities Osteoarthritis Index data of the Glucosamine Unum In Die (once-a-day) Efficacy trial study by Herrero-Beaumont et al. Clinical effectiveness was converted into utility scores to allow for the computation of cost per quality-adjusted life year (QALY) For the three treatment arms Incremental Cost-Effectiveness Ratio were calculated and statistical uncertainty was explored using a bootstrap simulation. RESULTS: In terms of mean utility score at baseline, 3 and 6 months, no statistically significant difference was observed between the three groups. When considering the mean utility score changes from baseline to 3 and 6 months, no difference was observed in the first case but there was a statistically significant difference from baseline to 6 months with a p-value of 0.047. When comparing GS with paracetamol, the mean baseline incremental cost-effectiveness ratio (ICER) was dominant and the mean ICER after bootstrapping was -1376 euro/QALY indicating dominance (with 79% probability). When comparing GS with PBO, the mean baseline and after bootstrapping ICER were 3617.47 and 4285 euro/QALY, respectively. CONCLUSION: The results of the present cost-effectiveness analysis suggested that GS is a highly cost-effective therapy alternative compared with paracetamol and PBO to treat patients diagnosed with primary knee 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".