Angiotensin converting enzyme inhibitors and risk of lung cancer: population based cohort study
Bibliographic record
Abstract
OBJECTIVE: To determine whether the use of angiotensin converting enzyme inhibitors (ACEIs), compared with use of angiotensin receptor blockers, is associated with an increased risk of lung cancer. DESIGN: Population based cohort study. SETTING: United Kingdom Clinical Practice Research Datalink. PARTICIPANTS: A cohort of 992 061 patients newly treated with antihypertensive drugs between 1 January 1995 and 31 December 2015 was identified and followed until 31 December 2016. MAIN OUTCOME MEASURES: Cox proportional hazards models were used to estimate adjusted hazard ratios with 95% confidence intervals of incident lung cancer associated with the time varying use of ACEIs, compared with use of angiotensin receptor blockers, overall, by cumulative duration of use, and by time since initiation. RESULTS: 1.2 per 1000 person years; hazard ratio 1.14, 95% confidence interval 1.01 to 1.29), compared with use of angiotensin receptor blockers. Hazard ratios gradually increased with longer durations of use, with an association evident after five years of use (hazard ratio 1.22, 1.06 to 1.40) and peaking after more than 10 years of use (1.31, 1.08 to 1.59). Similar findings were observed with time since initiation. CONCLUSIONS: In this population based cohort study, the use of ACEIs was associated with an increased risk of lung cancer. The association was particularly elevated among people using ACEIs for more than five years. Additional studies, with long term follow-up, are needed to investigate the effects of these drugs on incidence of lung cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".