Lung cancer risk in never-smokers: a population-based case-control study of epidemiologic risk factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: We conducted a case-control study in the greater Toronto area to evaluate potential lung cancer risk factors including environmental tobacco smoke (ETS) exposure, family history of cancer, indoor air pollution, workplace exposures and history of previous respiratory diseases with special consideration given to never smokers. METHODS: 445 cases (35% of which were never smokers oversampled by design) between the ages of 20-84 were identified through four major tertiary care hospitals in metropolitan Toronto between 1997 and 2002 and were frequency matched on sex and ethnicity with 425 population controls and 523 hospital controls. Unconditional logistic regression models were used to estimate adjusted odds ratios (OR) and 95% confidence intervals (CI) for the associations between exposures and lung cancer risk. RESULTS: Any previous exposure to occupational exposures (OR total population 1.6, 95% CI 1.4-2.1, OR never smokers 2.1, 95% CI 1.3-3.3), a previous diagnosis of emphysema in the total population (OR 4.8, 95% CI 2.0-11.1) or a first degree family member with a previous cancer diagnosis before age 50 among never smokers (OR 1.8, 95% CI 1.0-3.2) were associated with increased lung cancer risk. CONCLUSIONS: Occupational exposures and family history of cancer with young onset were important risk factors among never smokers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 it