Welding and Lung Cancer in a Pooled Analysis of Case-Control Studies
Bibliographic record
Abstract
Several epidemiologic studies have indicated an increased risk of lung cancer among welders. We used the SYNERGY project database to assess welding as a risk factor for developing lung cancer. The database includes data on 15,483 male lung cancer cases and 18,388 male controls from 16 studies in Europe, Canada, China, and New Zealand conducted between 1985 and 2010. Odds ratios and 95% confidence intervals between regular or occasional welding and lung cancer were estimated, with adjustment for smoking, age, study center, and employment in other occupations associated with lung cancer risk. Overall, 568 cases and 427 controls had ever worked as welders and had an odds ratio of developing lung cancer of 1.44 (95% confidence interval: 1.25, 1.67) with the odds ratio increasing for longer duration of welding. In never and light smokers, the odds ratio was 1.96 (95% confidence interval: 1.37, 2.79). The odds ratios were somewhat higher for squamous and small cell lung cancers than for adenocarcinoma. Another 1,994 cases and 1,930 controls had ever worked in occupations with occasional welding. Work in any of these occupations was associated with some elevation of risk, though not as much as observed in regular welders. Our findings lend further support to the hypothesis that welding is associated with an increased risk of lung cancer.
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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 | 0.032 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.022 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".