Calcium channel blockers and cancer: a risk analysis using the UK Clinical Practice Research Datalink (CPRD)
Bibliographic record
Abstract
OBJECTIVE: The evidence of an association between calcium channel blockers (CCBs) and cancer is conflicting. The objective of the present study was to evaluate the risk of cancer (all, breast, prostate and colon cancers) in association with exposure to CCB. METHODS: This is a population-based cohort study in patients exposed to CCBs from across the UK, using two comparison cohorts: (1) patients with no exposure to CCB (non-CCB) matched on age and gender and (2) unmatched patients unexposed to CCB and at least one other antihypertensive (AHT) prescription. Cancer incidence rates computed in the exposed and the two unexposed groups were compared using HRs and 95% CIs obtained from multivariate Cox regression analyses. RESULTS: Overall, 150,750, 557,931 and 156,966 patients were included, respectively, in the CCB, non-CCB and AHT cohorts. Crude cancer incidence rates per 1000 person-years were 16.51, 15.75 and 10.62 for the three cohorts, respectively. Adjusted HRs (CI) for all cancers comparing CCB, non-CCB and AHT cohorts were 0.88 (0.86 to 0.89) and 1.01 (0.98 to 1.04), respectively. Compared to the AHT cohort, adjusted HRs (CI) for breast, prostate and colon cancer for the CCB cohort were 0.95 (0.87 to 1.04), 1.07 (0.98 to 1.16) and 0.89 (0.81 to 0.98), respectively. Analyses by duration of exposure to CCB did not show excess risk. CONCLUSIONS: This large population-based study provides strong evidence that CCB use is not associated with an increased risk of cancer. The analyses yielded robust results across all types of cancer and different durations of exposure to CCBs.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".