Maternal Occupational Exposure to Extremely Low Frequency Magnetic Fields During Pregnancy and Childhood Leukemia
Bibliographic record
Abstract
BACKGROUND: Pregnancy is a target period for events that could induce childhood leukemia. There has been little attention to possible effects of maternal occupational exposure to extremely low frequency magnetic fields (ELF-MF) during pregnancy. METHODS: We conducted a population-based, case-control study of 491 incident cases of acute lymphoblastic leukemia in children 0-9 years of age, matched on age and sex to 491 healthy controls. Cases were diagnosed in the Province of Québec between 1980 and 1993. Mothers were interviewed to obtain detailed prenatal occupational history; individual exposure to ELF-MF was estimated based on a method we recently developed. We used 3 metrics for analyzing exposure: cumulative, average and maximum levels. Analyses were carried out among all study women and among working women only. RESULTS: Comparing the highest 10% of exposed mothers to the others, the risk of leukemia among offspring was moderately increased by using any metric, in all women and among working women only. The highest odds ratio of 2.5 (95% confidence interval = 1.2-5.0) was found for maximum exposure attained in an occupation (>/=0.4 microtesla). CONCLUSIONS: Our results are compatible with an increased risk of childhood leukemia among children whose mothers were exposed to the highest occupational levels of ELF-MF during pregnancy.
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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 | 0.000 | 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".