Statin Use and Survival from Lung Cancer: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Preclinical evidence from lung cancer cell lines and animal models suggest that statins could have anticancer properties. We investigated whether statin users had reduced risk of cancer-specific mortality in a population-based cohort of lung cancer patients. METHODS: Newly diagnosed lung cancer patients, from 1998 to 2009, were identified from English cancer registry data and linked to the UK Clinical Practice Research Datalink, providing prescription records, and to Office of National Statistics mortality data up to 2012. Cox regression models were used to calculate HRs for cancer-specific mortality and 95% confidence intervals (CI) by statin use before and after diagnosis, and to adjust these HRs for potential confounders. RESULTS: In 3,638 lung cancer patients, there was some evidence that statin use after diagnosis was associated with reduced lung cancer-specific mortality (adjusted HR, 0.89; 95% CI, 0.78-1.02; P = 0.09). Associations were more marked after 12 prescriptions (adjusted HR, 0.81; 95% CI, 0.67-0.98; P = 0.03) and when lipophilic statins were investigated (adjusted HR, 0.81; 95% CI, 0.70-0.94; P = 0.01), but were attenuated in some sensitivity analyses. Furthermore, in 11,051 lung cancer patients, statin use before diagnosis was associated with reduced lung cancer-specific mortality (adjusted HR, 0.88; 95% CI, 0.83-0.93; P < 0.001). CONCLUSIONS: There was some evidence that lung cancer patients who used statins, and particularly simvastatin, had reduced rates of cancer-specific mortality. IMPACT: These findings should first be confirmed in observational studies, but provide some support for conducting randomized controlled trials of simvastatin as adjuvant cancer therapy in lung cancer patients.
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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 | 0.002 |
| 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 | 0.001 |
| 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".