Bibliographic record
Abstract
BACKGROUND: Numerous observational studies show that statin use is associated with lower risk of osteoporotic fractures. However, a causal relationship is not supported by data from randomized trials. Unmeasured confounding is implicated as a likely culprit for the controversy because of failure to measure and adjust for patient-level tendencies to engage in healthy behaviors. However, an alternative explanation is selection bias because of the inclusion of prevalent users of statins in the analysis. The relative importance of either bias has not been investigated in a quantitative sensitivity analysis. METHODS: We conducted a systematic review to summarize the pattern of association between statin use and fracture risk in observational studies. Our objective was to quantify the magnitude of unmeasured confounding and selection bias in a sensitivity analysis. RESULTS: In 17 published studies, the pooled relative risk for the association between current use of statins and fracture risk was 0.75 (95% confidence interval = 0.66-0.85). Upon adjustment for individual-level use of preventative health services, the pooled relative risk shifted by less than 5% on the log scale. However, a sensitivity analysis for selection bias revealed that moderate levels of bias could eliminate the association between statins and fracture risk. CONCLUSIONS: It appears that confounding from unmeasured variables cannot explain the protective association between statins and fractures that has been observed in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".