Leukemia in electric utility workers: The evaluation of alternative indices of exposure to 60 Hz electric and magnetic fields
Bibliographic record
Abstract
BACKGROUND: Epidemiological studies have inconsistently demonstrated a positive relationship between magnetic and/or electric fields and leukemia. Although exposure to both 60 Hz electric and magnetic fields can be characterized in many ways, to date, risk assessment has been performed by using only a limited number of exposure indices. METHODS: The associations between adult leukemia and indices of electric and magnetic fields were explored within a nested case-control study of 31,453 Ontario electric utility workers. RESULTS: The percentage of time spent above electric field thresholds of 20 and 39 V/m was predictive of leukemia risk after adjusting for duration of employment and the arithmetic mean exposure to both electric and magnetic fields (P<0.05). Duration of employment was strongly associated with an increased risk of leukemia. Those who had worked for at least 20 years, and were in the highest tertiles of percentage of time spent above 10 and 20 V/m had odds ratios of 10.17 (95% CI = 1.58-65.30) and 8.23 (95% CI = 1. 24-54.43), respectively, when compared to those in the lowest tertile. Nonsignificant elevations in risk were observed between indices of magnetic fields and leukemia. CONCLUSIONS: Our results support the hypothesis that electric fields act as a promoting agent in the etiology of adult leukemia. Exposure assessment based on alternate indices of electric and magnetic fields should be incorporated into future occupational studies of cancer.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".