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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".