Effects of Glucosamine and Chondroitin Supplementation on Knee Osteoarthritis: An Analysis With Marginal Structural Models
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to estimate the effectiveness of the combination of glucosamine and chondroitin in relieving knee symptoms and slowing disease progression among patients with knee osteoarthritis (OA). METHODS: The 4-year followup data from the Osteoarthritis Initiative data set were analyzed. We used a "new-user" design, for which only participants who were not using glucosamine/chondroitin at baseline were included in the analyses (n = 1,625). Cumulative exposure was calculated as the number of visits when participants reported use of glucosamine/chondroitin. Knee symptoms were measured with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and structural progression was determined by measuring the joint space width (JSW). To control for the time-varying confounders that might be influenced by previous treatments, we used marginal structural models to estimate the effects on OA of using glucosamine/chondroitin for 3 years, 2 years, and 1 year. RESULTS: During the study period, 18% of the participants initiated treatment with glucosamine/chondroitin. After adjustment for potential confounders with marginal structural models, we found no clinically significant differences between users at all assessments and never-users of glucosamine/chondroitin in WOMAC pain (β = 0.68 [95% confidence interval (95% CI) -0.16 to 1.53]), WOMAC stiffness (β = 0.41 [95% CI 0 to 0.82]), and WOMAC function (β = 1.28 [95% CI -1.23 to 3.79]) or JSW (β = 0.11 [95% CI -0.21 to 0.44]). CONCLUSION: Use of glucosamine/chondroitin did not appear to relieve symptoms or modify disease progression among patients with radiographically confirmed OA. Our findings are consistent with the results of meta-analyses of clinical trials and extend those results to a more general population with 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 | 0.060 | 0.073 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.018 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".