Interactions between occupational exposure to extremely low frequency magnetic fields and chemicals for brain tumor risk in the INTEROCC study
Bibliographic record
Abstract
Background: Brain tumors are a serious, often fatal disease with few established risk factors. Although ionizing radiation has been clearly linked with brain tumors, there are a number of other environmental and occupational possible risk factors. There may also be interactions between occupational chemical and physical agents for brain tumors however the past epidemiological literature has been sparse. One previous epidemiological study examined potential interactive effects between occupational exposure to extremely low frequency magnetic fields (ELF-MF) and chemical agents with various interactive effects observed. Aims: The aim of this paper was to examine the possible joint effects of occupational agents for brain tumors including occupational ELF-MF and chemicals in the large-scale INTEROCC study. Methods: INTEROCC includes seven participating countries (Australia, Canada, France, Germany, Israel, New Zealand, United Kingdom) from the parent INTERPHONE study. Cases of primary brain glioma and meningioma aged at least 20 years were recruited between 2000 and 2004. Detailed occupational history data was collected for jobs held for at least six months. Job titles were coded into standard international occupational classifications and estimates of ELF-MF and chemical exposure were assigned based on job exposure matrices. Odds ratios (and 95% confidence intervals) for single and co-occupational exposures were calculated according to a common reference category. Results: Data on a total of 3,978 brain tumor cases, including 2,054 gliomas and 1,924 meningiomas, were analyzed with 5,601 control subjects. A number of interactions between ELF-MF and chemicals, particularly metals, were observed for glioma. Possible methodological factors underlying findings are explored. Conclusions: Further research examining possible joint effects of occupational agents for brain tumors with refined assessments of occupational exposure in other large-scale studies is warranted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".