Brain cancer and occupational exposure to magnetic fields among men: results from a Canadian population-based case-control study
Bibliographic record
Abstract
BACKGROUND: The relationship between occupational exposure to magnetic fields and brain cancer in men was investigated using population-based case-control data collected in eight Canadian provinces. Emphasis was placed on examining the variations in risk across different histological types. METHODS: A list of occupations was compiled for 543 cases and 543 controls that were individually matched by age. Occupations were categorized according to their average magnetic field exposure through blinded expert review (<0.3, 0.3-<0.6, and > or = 0.6 microT). In total, 133 cases (14%) and 123 controls (12%) were estimated to have at least one occupation whereby magnetic field exposures exceeded 0.3 microT. Odds ratios (OR) were generated using conditional logistic regression, and were adjusted for suspected occupational risk factors for brain cancer. RESULTS: A non-significantly increased risk of brain cancer was observed among men who had ever held a job with an average magnetic field exposure >0.6 microT relative to those with exposures <0.3 microT (OR = 1.33, 95% CI : 0.75-2.36). A more pronounced risk was observed among men diagnosed with glioblastoma multiforme (OR = 5.36, 95% CI : 1.16-24.78). Moreover, a cumulative time weighted index score of magnetic field exposure was significantly related to glioblastoma multiforme (P = 0.02). In contrast, magnetic field exposures were not associated with astrocytoma or other brain cancers. CONCLUSIONS: Our findings support the hypothesis that occupational magnetic field exposure increases the risk of glioblastoma multiforme.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".