Relationships between the phenotypes of lung cancer, occupational exposure to inhaled particles, and tobacco smoking
Bibliographic record
Abstract
Introduction: The objective of this thesis was to study the association between tobacco smoking and occupational exposure to asbestos and crystalline silica with the phenotypes of lung cancer. The second objective was to assess the effect modification of the association between tobacco smoking and the phenotypes of lung cancer by occupational exposure to asbestos or to crystalline silica. Methods : The CaProMat study is a pooled retrospective case-only study consisted of9,623 French and Canadian lung cancer cases. All lung cancer cases were histologically confirmed. Data were collected from medical records and through standardized questionnaires. Two job-exposure matrices (JEMs) were used to assess the occupational exposure to asbestos and to crystalline silica. Results: We did not identify a difference of prevalence of occupational exposure to asbestos according to histological type. For crystalline silica, a borderline excess of prevalence of exposure was observed for squamous cell carcinoma. The prevalence of occupational exposure was maximized among lung cancer cases diagnosed between 50 and 59 years for asbestos and less than 50 years for crystalline silica. Additional exposure to either asbestos or crystalline silica did not modify the effect of tobacco smoking for histological type, tumor location or age at diagnosis. Conclusions: The histological type, tumor location, and age at diagnosis cannot beused as an indicator for the occupational exposure to asbestos or to crystalline silica.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".