BioCAST/IFCT-1002: epidemiological and molecular features of lung cancer in never-smokers
Bibliographic record
Abstract
Lung cancer in never-smokers (LCINS) (fewer than 100 cigarettes in lifetime) is considered as a distinct entity and harbours an original molecular profile. However, the epidemiological and molecular features of LCINS in Europe remain poorly understood. All consecutive newly diagnosed LCINS patients were included in this prospective observational study by 75 participating centres during a 14-month period. Each patient completed a detailed questionnaire about risk factor exposure. Biomarker and pathological analyses were also collected. We report the main descriptive overall results with a focus on sex differences. 384 patients were included: 65 men and 319 women. 66% had been exposed to passive smoking (significantly higher among women). Definite exposure to main occupational carcinogens was significantly higher in men (35% versus 8% in women). A targetable molecular alteration was found in 73% of patients (without any significant sex difference): EGFR in 51%, ALK in 8%, KRAS in 6%, HER2 in 3%, BRAF in 3%, PI3KCA in less than 1%, and multiple in 2%. We present the largest and most comprehensive LCINS analysis in a European population. Physicians should track occupational exposure in men (35%), and a somatic molecular alteration in both sexes (73%).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".