Long-Term Use of Statins and Risk of Colorectal Cancer
Bibliographic record
Abstract
OBJECTIVES: We conducted a population-based cohort study to determine the effect of long-term regular use of statins on the risk of colorectal cancer (CRC). METHODS: Individuals who were dispensed statins regularly were identified from Manitoba's population-based prescription drug database and followed up until diagnosis of CRC, migration out of province, death, or December 2005. The incidence of CRC in this group was compared with that among individuals who were never dispensed statins. Stratified analysis was performed to determine the risk after 5 years of regular statin use. Multivariate Poisson regression models were used to adjust for potential confounding by age, sex, and history of diabetes, inflammatory bowel disease, coronary heart disease, lower gastrointestinal endoscopy, resective colorectal surgery, use of nonsteroidal anti-inflammatory drugs, hormone replacement therapy (among women), and median household income. The dose effect was evaluated in defined daily dose units. RESULTS: In total, 35,739 individuals were dispensed statins regularly. In all, 10,287 (49% males; 51% females) long-term (>or=5 years) regular statin users were followed up for up to 5 additional years. In multivariate analysis, the incidence rate ratio (IRR) of CRC among those dispensed statins regularly compared with those who were never dispensed statins (n=377,532) was 1.13 (95% confidence interval (CI): 1.02-1.25). The CRC risk among the long-term regular statin users was similar to that for individuals never dispensed statins (IRR, 0.89; 95% CI: 0.70-1.13). A statistically nonsignificant risk reduction was observed among high-dose long-term regular statin users. CONCLUSIONS: These findings suggest that long-term regular use of statins for the current clinical indications does not protect against CRC. The benefit of high-dose long-term statin use needs further evaluation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".