Calcium Channel Blockers and the Risk for Lung Cancer: A Population-Based Nested Case-Control Study
Bibliographic record
Abstract
BACKGROUND: It has been suggested that calcium channel blockers (CCBs) may increase the risk of lung cancer; however, current evidence is conflicting and limited. OBJECTIVE: Investigate the associations between CCB use and lung cancer. METHODS: We conducted a population-based nested case-control study. A cohort was formed of patients prescribed their first antihypertensive agent from 2000 to 2014. CCB exposure information was obtained by identification of all prescriptions dispensed during study follow-up. Cases were patients newly diagnosed with lung cancer during follow-up. Each case was matched with 10 controls by age, sex, calendar year of cohort entry, and duration of follow-up. Multivariate conditional logistic regression was used to estimate odds ratios (ORs) with 95% CIs of lung cancer associated with ever use of CCBs. RESULTS: During a median follow-up of 6.2 years, we identified 4174 cases of lung cancer. Ever use of CCBs was associated with an increased risk of lung cancer (adjusted OR = 1.13; 95% CI = 1.06-1.21), when compared with the use of other antihypertensive drugs. A duration-response relation was observed, with the ORs gradually increasing with longer cumulative duration of CCB use (<5 years: OR = 1.12, 95% CI = 1.04-1.20; 5-10 years: OR = 1.22, 95% CI = 1.07-1.40; >10 years: OR = 1.33, 95% CI = 0.90-1.96; P trend < 0.001). Conclusion and Relevance: The results of this large population-based study indicate that the use of CCBs is associated with a modest but significant increase in the risk of lung cancer. This association appeared to increase with longer duration of use.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".