Occupational exposures and odds of gastric cancer: a StoP project consortium pooled analysis
Bibliographic record
Abstract
BACKGROUND: Gastric cancer pathogenesis represents a complex interaction of host genetic determinants, microbial virulence factors and environmental exposures. Our primary aim was to determine the association between occupations/occupational exposures and odds of gastric cancer. METHODS: We conducted a pooled-analysis of individual-level data harmonized from 11 studies in the Stomach cancer Pooling Project. Multivariable logistic regression was used to estimate the odds ratio (OR) of gastric cancer adjusted for relevant confounders. RESULTS: A total of 5279 gastric cancer cases and 12 297 controls were analysed. There were higher odds of gastric cancer among labour-related occupations, including: agricultural and animal husbandry workers [odds ratio (OR) 1.33, 95% confidence interval (CI): 1.06-1.68]; miners, quarrymen, well-drillers and related workers (OR 1.70, 95% CI: 1.01-2.88); blacksmiths, toolmakers and machine-tool operators (OR 1.41, 95% CI: 1.05-1.89); bricklayers, carpenters and construction workers (OR 1.30, 95% CI: 1.06-1.60); and stationary engine and related equipment operators (OR 6.53, 95% CI: 1.41-30.19). The ORs for wood-dust exposure were 1.51 (95% CI: 1.01-2.26) for intestinal-type and 2.52 (95% CI: 1.46-4.33) for diffuse-type gastric cancer. Corresponding values for aromatic amine exposure were 1.83 (95% CI: 1.09-3.06) and 2.92 (95% CI: 1.36-6.26). Exposure to coal derivatives, pesticides/herbicides, chromium, radiation and magnetic fields were associated with higher odds of diffuse-type, but not intestinal-type gastric cancer. CONCLUSIONS: Based on a large pooled analysis, we identified several occupations and related exposures that are associated with elevated odds of gastric cancer. These findings have potential implications for risk attenuation and could be used to direct investigations evaluating the impact of targeted gastric cancer prevention/early detection programmes based on occupation.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".