Cardiac glycosides use and the risk of lung cancer: a nested case–control study
Bibliographic record
Abstract
BACKGROUND: Two studies have reported statistically significant associations between the use of cardiac glycosides (CGs) and an increased risk of lung cancer. However, these studies had a number of methodological limitations. Thus, the objective of this study was to assess this association in a large population-based cohort of patients. METHODS: We used the United Kingdom Clinical Practice Research Datalink (CPRD) to identify a cohort of patients, at least 40 years of age, newly-diagnosed with heart failure, or supra-ventricular arrhythmia. A nested case-control analysis was conducted where each incident case of lung cancer identified during follow-up was randomly matched with up to 10 controls. Exposure to CGs was assessed in terms of ever use, cumulative duration of use and cumulative dose. Rate ratios (RRs) with 95% confidence intervals (CIs) were estimated using conditional logistic regression after adjusting for potential confounders. RESULTS: A total of 129,002 patients were included, and followed for a mean (SD) of 4.7 (3.8) years. During follow-up, 1237 patients were newly-diagnosed with lung cancer. Overall, ever use of CGs was not associated with an increased risk of lung cancer when compared to never use (RR = 1.09, 95% CI: 0.94-1.26). In addition, no dose-response relationship was observed in terms of cumulative duration of use and cumulative dose with all RRs around the null value across quartile categories. CONCLUSION: The results of this large population-based study indicate that the use of CGs is not associated with an increased risk of lung cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".