Smoking Is the Most Significant Modifiable Lung Cancer Risk Factor in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To assess lung cancer risk in systemic lupus erythematosus (SLE), relative to demographics, drug exposures, smoking, and disease activity. METHODS: We analyzed data from 14 SLE cohorts. We calculated adjusted HR estimates for lung cancer in SLE, relative to demographics, smoking, time-dependent medication exposures, and cumulative disease activity [mean adjusted SLE Disease Activity Index (SLEDAI) scores]. This project was approved by the ethics boards of all participating institutions, including the Institutional Review Board of the McGill University Health Centre. The ethics approval number for the Cancer Risk study is GEN-06-031. RESULTS: Within these 14 SLE cohorts, 49 incident lung cancers occurred. Among lung cancer cases, 59.0% were in the highest SLEDAI quartile at baseline versus 40.8% of lung cancer-free SLE controls. The vast majority (84.2%) of SLE lung cancer cases were ever-smokers at baseline, versus 40.1% of those without lung cancer. In adjusted models, the principal factors associated with lung cancer were ever smoking (at cohort entry) and current age. Estimated adjusted effects of all drugs were relatively imprecise, but did not point toward any drug exposures as strong lung cancer risk factors. CONCLUSION: We saw no clear evidence for drugs as a trigger for lung cancer risk in SLE, although drug risk estimates were relatively imprecise. Smoking may be the most significant modifiable lung cancer risk factor in SLE.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".