Lung cancer, genetic predisposition and smoking: the Nordic Twin Study of Cancer
Bibliographic record
Abstract
Background We aimed to disentangle genetic and environmental causes in lung cancer while considering smoking status. Methods Four Nordic twin cohorts (43 512 monozygotic (MZ) and 71 895 same sex dizygotic (DZ) twin individuals) had smoking data before cancer diagnosis. We used time-to-event analyses accounting for censoring and competing risk of death to estimate incidence, concordance risk and heritability of liability to develop lung cancer by smoking status. Results During a median of 28.5 years of follow-up, we recorded 1508 incident lung cancers. Of the 30 MZ and 28 DZ pairs concordant for lung cancer, nearly all were current smokers at baseline and only one concordant pair was seen among never smokers. Among ever smokers, the case-wise concordance of lung cancer, that is the risk before a certain age conditional on lung cancer in the co-twin before that age, was significantly increased compared with the cumulative incidence for both MZ and DZ pairs. This ratio, the relative recurrence risk, significantly decreased by age for MZ but was constant for DZ pairs. Heritability of lung cancer was 0.41 (95% CI 0.26 to 0.56) for currently smoking and 0.37 (95% CI 0.25 to 0.49) for ever smoking pairs. Among smoking discordant pairs, the pairwise HR for lung cancer of the ever smoker twin compared to the never smoker co-twin was 5.4 (95% CI 2.1 to 14.0) in MZ pairs and 5.0 (95% CI 3.2 to 7.9) in DZ pairs. Conclusions The contribution of familial effects appears to decrease by age. The discordant pair analysis confirms that smoking causes 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".