Long-term use of thiazolidinediones and fractures in type 2 diabetes: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Rosiglitazone and pioglitazone may increase the incidence of fractures. We aimed to determine systematically the risk of fractures associated with thiazolidinedione therapy and to evaluate the effect of the therapy on bone density. METHODS: We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), other trial registries and product information sheets through June 2008. We selected long-term (> or = 1 year) randomized controlled trials involving patients with type 2 diabetes and controlled observational studies that described the risk of fractures or changes in bone density with thiazolidinediones. We calculated pooled odds ratios (ORs) for fractures and the weighted mean difference in bone density. RESULTS: We analyzed data from 10 randomized controlled trials involving 13 715 participants and from 2 observational studies involving 31 679 participants. Rosiglitazone and pioglitazone were associated with a significantly increased risk of fractures overall in the 10 randomized controlled trials (OR 1.45, 95% confidence interval [CI] 1.18-1.79; p < 0.001). Five randomized controlled trials showed a significantly increased risk of fractures among women (OR 2.23, 95% CI 1.65-3.01; p < 0.001) but not among men (OR 1.00, 95% CI 0.73-1.39; p = 0.98). The 2 observational studies demonstrated an increased risk of fractures associated with rosiglitazone and pioglitazone. Bone mineral density in women exposed to thiazolidinediones was significantly reduced at the lumbar spine (weighted mean difference -1.11%, 95% CI -2.08% to -0.14%; p = 0.02) and hip (weighted mean difference -1.24%, 95%CI -2.34% to -0.67%; p < 0.001) in 2 randomized controlled trials. INTERPRETATION: Long-term thiazolidinedione use doubles the risk of fractures among women with type 2 diabetes, without a significant increase in risk of fractures among men with type 2 diabetes.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".