Metformin as adjuvant therapy in obese knee osteoarthritis patients
Bibliographic record
Abstract
AIMS: This study aimed at investigating the efficacy of metformin as adjuvant therapy for obese knee osteoarthritis (OA) patients, considering its anti-inflammatory and cartilage-protective effects. PATIENTS AND METHODS: In this randomized, double-blind, placebo-controlled study, 50 obese knee OA patients were assigned randomly to two groups, the metformin group (n = 25) which was treated with metformin 500 mg orally BID plus celecoxib 200 mg orally once daily, and the placebo group (n = 25) which was treated with placebo tablets BID plus celecoxib 200 mg orally once daily for 12 weeks. Cartilage Oligomeric Matrix Protein (COMP), C-terminal cross-linked telopeptide of type I collagen (CTX-1), and Interleukin 1-beta (IL-1β) serum levels were measured, while Western Ontario and McMaster Universities Arthritis Index (WOMAC) score assessed knee pain, stiffness, and physical function at baseline and after 12 weeks. RESULTS: Following a 12-week treatment, the metformin group exhibited significantly reduced levels of COMP, CTX-1, and IL-1β in the serum compared to the placebo group (p = 0.0081, p = 0.0106, and p = 0.0223, respectively). Furthermore, metformin group produced significant improvements in WOMAC total scale (p < 0.0001), specifically in knee pain, stiffness, and physical function compared to placebo group (p < 0.0001, p < 0.0001, and p < 0.0001, respectively). CONCLUSION: Metformin as an adjuvant therapy in obese knee OA patients may have beneficial effects on cartilage degradation and inflammation, as evidenced by the significant decreases in serum COMP, CTX-1, and IL-1β levels. Additionally, metformin may improve clinical outcomes, as shown by the significant improvements in WOMAC scores. GOV ID: NCT05638893/Registered December 6, 2022 - Retrospectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".