Cancers des voies respiratoires attribuables aux expositions professionnelles : quels coûts pour la société française ?
Bibliographic record
Abstract
OBJECTIVE: To estimate the social cost of respiratory cancers attributable to occupational risk factors in France in 2010. METHODS: We estimated the number of cases of respiratory cancers attributable to each identified occupational risk factor according to the attributable fractions method. We also estimated direct (costs of hospital stays, drugs, outpatient care) and indirect costs (production losses) related to morbidity (absenteeism and presenteeism) and mortality (years of lost production). Production losses for paid work and unpaid domestic activities were taken into account. RESULTS: The social cost of respiratory cancers (lung, larynx, sinonasal, pleural mesothelioma) attributable to exposure to asbestos, chromium, diesel engine exhaust, polycyclic aromatic hydrocarbons, painting occupations (unidentified carcinogen), crystalline silica, wood and leather dust in France in 2010 was estimated to be between €960 and 1,866 million. The cost of lung cancer represents between €804 and 1,617 million. The three risk factors with the greatest impact are asbestos (€530 to 890 million), diesel engine exhaust (€227 to 394 million), and crystalline silica (€116 to 268 million). CONCLUSION: These results provide a conservative estimate of the public health and economic burden of respiratory cancers attributable to occupational risk factors from a societal perspective.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".