OCCUPATIONAL CARCINOGENS IN EUROPE: PAST AND PRESENT EXPOSURES IN RELATION TO LUNG CANCER RISK
Bibliographic record
Abstract
Lung cancer isthe most common occupational cancer and it has been estimated that around 15%of lung cancer among men and 5% in women are due to occupational exposures. In the SYNERGYproject, we have pooled data from fourteen case-control studies conductedbetween 1985 and 2010 in Europe and Canada, including 16,901 lung cancer casesand 20,965 controls with detailed information on tobacco habits and lifetimeoccupations. A quantitative job-exposure-matrix (SYN-JEM) was developed toestimate job-, time period-, and region-specific exposure levels for asbestos,respirable crystalline silica (RCS), chromium, nickel, and benzo (a) pyrene.Exposure levels were calculated for each subject by linking SYN-JEM withindividual occupational histories. The lung cancerrisks were estimated for the single carcinogens and in association withsmoking. The detailed smoking information allowed a precise adjustment forsmoking, and the large dataset enabled us to estimate the risks also inrelevant subgroups like never smokers. Overall, we observed a dose-dependentincrease of the lung cancer risk for all carcinogens that was more pronouncedfor squamous cell carcinoma and small cell lung cancer than for adenocarcinomaof the lung. Exposure trendsin Europe have overall been decreasing in the past 50 years. This phenomenon isthe result of long-term investments in occupational health and safety, i.e.training, surveillance, research, and improved technology. In light of the lastyears economic crisis around the world it is important to ensure that thesepositive trends continue and spread to all countries in order to improveoccupational health worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".