Thiazolidinediones and Fractures in Men and Women
Bibliographic record
Abstract
BACKGROUND: Clinical trials and meta-analyses have found that rosiglitazone maleate, a thiazolidinedione that is prescribed for type 2 diabetes mellitus, increases the risk of fractures in women. The association between the use of thiazolidinediones and fractures in men and women is not adequately understood. METHODS: We conducted a prospective cohort study. The primary outcome was peripheral fractures in men and women who were exposed to thiazolidinediones compared with sulfonylureas. We studied 84 339 patients from British Columbia, Canada, who began treatment with a thiazolidinedione or a sulfonylurea. The association between the use of thiazolidinediones and fractures was examined using multivariate-adjusted Cox models. RESULTS: The mean age of the patients in the study was 59 years, and 43% were women. In this cohort, treatment with a thiazolidinedione was associated with a 28% increased risk of peripheral fractures compared with treatment with a sulfonylurea (hazard ratio [HR], 1.28; 95% confidence interval [CI], 1.10-1.48). The use of pioglitazone hydrochloride was associated with an increased risk of peripheral fracture of 77% in women (HR, 1.76; 95% CI 1.32-2.38). Compared with exposure to sulfonylureas, exposure to pioglitazone was associated with more peripheral fractures in men (HR, 1.61; 95% CI 1.18-2.20), but we did not observe a similar association with exposure to rosiglitazone (HR, 1.00; 95% CI, 0.75-1.34). CONCLUSIONS: Both men and women who take thiazolidinediones could be at increased risk of fractures. Pioglitazone may be more strongly associated with fractures than rosiglitazone. Larger observational studies are needed, and fracture data from clinical trials need to be fully published so that fracture risks can be known with greater certainty.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".