Association of higher potency statin use with risk of osteoporosis and fractures in patients with stroke in a Korean nationwide cohort study
Bibliographic record
Abstract
This population-based cohort study aimed to evaluate the risk of osteoporosis and fractures associated with higher-potency statin use compared to lower-potency statin use in patients with stroke, using data from the Health Insurance and Review Assessment database of South Korea (2010-2019). Patients who received statin within 30 days after hospitalization for a new-onset stroke (n = 276,911) were divided into higher-potency (n = 212,215, 76.6%) or lower-potency (n = 64,696, 23.4%) statin initiation groups. The primary outcome was a composite of osteoporosis and osteoporotic fractures. Secondary outcomes were individual components of the primary outcome, including osteoporosis, vertebral fracture, hip fracture, and non-hip non-vertebral fracture. Cox proportional hazard models weighted by standardized morbidity ratios were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). The risk of the composite outcome (HR 0.95, 95% CI 0.93-0.97), osteoporosis (0.93, 0.90-0.96), vertebral fracture (0.95, 0.91-0.99), and hip fracture (0.89, 0.84-0.95) were significantly lower in higher-potency statin users, while the risk for non-hip non-vertebral fracture was not significant (0.98, 0.95-1.02). The use of higher-potency statins compared to lower-potency statins was associated with a lower risk of osteoporosis, vertebral fracture, and hip fracture in patients with stroke.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".