Antihypertensive medications and risk of colorectal cancer in British Columbia
Bibliographic record
Abstract
Introduction: There is conflicting evidence for the association between antihypertensive medications and colorectal cancer risk, possibly reflecting methodological limitations of previously conducted studies. Here, we aimed to clarify associations between commonly prescribed antihypertensive medication classes and colorectal cancer risk in a large, retrospective, cohort study. Methods: Using linked administrative data between 1996 and 2017 from British Columbia, we identified a cohort of 1,693,297 men and women who were 50 years of age or older, initially cancer-free and nonusers of antihypertensive medications. Medication use was parameterized as ever use, cumulative duration, and cumulative dose. Cox proportional hazard models were used to estimate hazard ratios (HRs) and associated 95% confidence intervals (95% CIs) for associations of time-varying medication use [angiotensin-converting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), beta-blockers (BBs), calcium channel blockers (CCBs), and diuretics] with colorectal cancer risk. Results: There were 28,460 incident cases of colorectal cancer identified over the follow-up period (mean = 12.9 years). When medication use was assessed as ever/never, diuretics were associated with increased risk of colorectal cancer (HR 1.08, 95% CI 1.04–1.12). However, no similar association was observed with cumulative duration or cumulative dose of diuretics. No significant associations between the other four classes of medications and colorectal cancer risk were observed. Conclusion: No compelling evidence of associations between antihypertensive medications and colorectal cancer were observed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".