3-Hydroxy-3-Methylglutaryl Coenzyme A Reductase Inhibitors and the Risk of Cancer
Bibliographic record
Abstract
BACKGROUND: During the past 15 years there has been an exponential increase in the number of prescriptions for lipid-lowering drugs. Uncertainties remain about the long-term impact of these medications on cancer, which is particularly bothersome given that the duration of these treatments may extend for several decades. OBJECTIVE: To explore the association between 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors and cancer incidence. METHODS: Using the administrative health databases of the Régie de l'Assurance-Maladie du Québec we performed a nested case-control study. We selected a cohort of 6721 beneficiaries of the health care plan of Quebec who were free of cancer for at least 1 year at cohort entry, 65 years and older, and treated with lipid-modifying agents. Cohort members were selected between 1988 and 1994 and were followed up for a median period of 2.7 years. From the cohort, 542 cases of first malignant neoplasm were identified, and 5420 controls were randomly selected. Users of HMG-CoA reductase inhibitors were compared with users of bile acid-binding resins as to their risk of cancer. Specific cancer sites were also considered. RESULTS: Users of HMG-CoA reductase inhibitors were found to be 28% less likely than users of bile acid-binding resins to be diagnosed as having any cancer (rate ratio, 0.72; 95% confidence interval, 0.57-0.92). All specific cancer sites under study were found to be not or inversely associated with the use of HMG-CoA reductase inhibitors. CONCLUSION: The results of our study provide some degree of reassurance about the safety of HMG-CoA reductase inhibitors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".