Metformin use and associated risk of total joint replacement in patients with type 2 diabetes: a population-based matched cohort study
Bibliographic record
Abstract
BACKGROUND: It is uncertain whether metformin use is associated with reduced risk of joint replacement in patients with type 2 diabetes mellitus. We aimed to establish whether metformin use was associated with a reduced risk of total knee replacement (TKR) or total hip replacement (THR) among these patients. METHODS: We selected patients with type 2 diabetes mellitus that was diagnosed between 2000 and 2012 from the Taiwan National Health Insurance Research Database. We used prescription time-distribution matching and propensity-score matching to balance potential confounders between metformin users and nonusers. We assessed the risks of TKR or THR using Cox proportional hazards regression. RESULTS: We included 20 347 participants who were not treated with metformin and 20 347 who were treated with metformin, for a total of 40 694 participants (mean age 63 yr, standard deviation 11 yr; 49.8% were women) after prescription time-distribution matching. Compared with participants who did not use metformin, those who used metformin had lower risks of TKR or THR (adjusted hazard ratio [HR] 0.70, 95% confidence interval [CI] 0.60-0.81 for TKR or THR; adjusted HR 0.71, 95% CI 0.61-0.84 for TKR; adjusted HR 0.61, 95% CI 0.41-0.92 for THR) after adjustment for covariates. Propensity-score matching analyses (10 163 participants not treated with metformin v. 10 163 treated with metformin) and sensitivity analyses using inverse probability of treatment weighting and competing risk regression showed similar results. INTERPRETATION: Metformin use in patients with type 2 diabetes mellitus was associated with a significantly reduced risk of total joint replacement. Randomized controlled clinical trials in patients with osteoarthritis are warranted to determine whether metformin is effective in decreasing the need for joint replacement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".