Risks of second primary cancer among patients with major histological types of lung cancers in both men and women
Bibliographic record
Abstract
BACKGROUND: Patterns of second primary cancers (SPCs) following first primary lung cancers (FPLCs) may provide aetiological insights into FPLC. METHODS: Cases of FPLCs in 13 cancer registries in Europe, Australia, Canada, and Singapore were followed up from the date of FPLC diagnosis to the date of SPC diagnosis, date of death, or end of follow-up. Standardised incidence ratios (SIRs) were calculated to estimate the magnitude of SPC development following squamous cell carcinoma (SCC), small cell lung carcinoma (SCLC), and adenocarcinoma (ADC). RESULTS: Among SCC patients, male SIR=1.58 (95% confidence interval (CI)=1.50-1.66) and female SIR=2.31 (1.94-2.72) for smoking-related SPC. Among SCLC patients, the respective ratios were 1.39 (1.20-1.60) and 2.28 (1.73-2.95), and among ADC patients, they were 1.73 (1.57-1.90) and 2.24 (1.91-2.61). We also observed associations between first primary lung ADC and second primary breast cancer in women (SIR=1.25, 95% CI=1.05-1.48) and prostate cancer (1.56, 1.39-1.79) in men. CONCLUSION: The FPLC patients carried excess risks of smoking-related SPCs. An association between first primary lung ADC and second primary breast and ovarian cancer in women at younger age and prostate cancers in men may reflect an aetiological role of hormones in lung ADC.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".