Interactions between occupational exposure to extremely low frequency magnetic fields and chemicals for brain tumour risk in the INTEROCC study
Bibliographic record
Abstract
OBJECTIVES: In absence of clear evidence regarding possible effects of occupational chemical exposures on brain tumour aetiology, it is worthwhile to explore the hypothesis that such exposures might act on brain tumour risk in interaction with occupational exposure to extremely low frequency magnetic fields (ELF). METHODS: INTEROCC is a seven-country (Australia, Canada, France, Germany, Israel, New Zealand and UK), population-based, case-control study, based on the larger INTERPHONE study. Incident cases of primary glioma and meningioma were ascertained from 2000 to 2004. Job titles were coded into standard international occupational classifications and estimates of ELF and chemical exposures were assigned based on job-exposure matrices. Dichotomous indicators of cumulative ELF (≥50th vs <50th percentile, 1-4 year exposure time window) and chemical exposures (ever vs never, 5-year lag) were created. Interaction was assessed on both the additive and multiplicative scales. RESULTS: A total of 1939 glioma cases, 1822 meningioma cases and 5404 controls were included in the analysis, using conditional logistic regression. There was no clear evidence for interactions between ELF and any of the chemical exposures assessed for either glioma or meningioma risk. For glioma, subjects in the low ELF/metal exposed group had a lower risk than would be predicted from marginal effects. Results were similar according to different exposure time windows, to cut-points of exposure or in exposed-only analyses. CONCLUSIONS: There was no clear evidence for interactions between occupational ELF and chemical exposures in relation to glioma or meningioma risk observed. Further research with more refined estimates of occupational exposures is recommended.
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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 | 0.001 | 0.004 |
| 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) | 0.002 | 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".