P-333 Using CANJEM to examine the association between occupational exposure to selected metals, metalloids, and welding fumes and brain cancer in the INTEROCC pooled international case-control study
Why this work is in the frame
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Bibliographic record
Abstract
<h3>Introduction</h3> Exposure to metallic compounds may contribute to the etiology of brain cancer; however, few epidemiologic studies have examined this potential association. <h3>Objective</h3> To examine occupational exposure to 21 metallic compounds in relation to the risk of glioma and meningioma. <h3>Methods</h3> INTEROCC is an international consortium of seven brain cancer case-control studies using a common protocol. Among 1,917 glioma cases, 1,827 meningioma cases, and 5,475 controls in the pooled INTEROCC population, job histories were collected and transformed into histories of exposure to 21 metallic compounds by linkage to the Canadian job-exposure-matrix. Three metrics of exposure were calculated for each agent: ever exposed, duration of exposure, and cumulative exposure. Conditional logistic regression was used to estimate the odds ratios (ORs) and their 95% confidence intervals (95%CIs) for the association between the three metrics of exposure and both glioma and meningioma. <h3>Results</h3> There was no evidence of associations between our selected agents and glioma. There were positive associations, with ORs ranging from 1.20 to 2.40, between meningioma and several of the metallic compounds, most notably zinc compounds, lead fumes, chromium VI compounds, soldering fumes, metal oxide fumes, and soldering fumes. Overall, our results were similar to two previous studies based on INTEROCC that examined five of the metallic compounds included in this study, using a modified version of the Finish job-exposure-matrix. <h3>Conclusion</h3> Our results are suggestive of positive associations between exposure to metallic compounds, particularly metallic fumes, and meningioma, but not glioma.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it