Nighttime Exposure to Electromagnetic Fields and Childhood Leukemia: An Extended Pooled Analysis
Bibliographic record
Abstract
It has been hypothesized that nighttime bedroom measurements of extremely low frequency electromagnetic fields (ELF EMF) may represent a more accurate reflection of exposure and have greater biologic relevance than previously used 24-/48-hour measurements. Accordingly, the authors extended a pooled analysis of case-control studies on ELF EMF exposure and risk of childhood leukemia to examine nighttime residential exposures. Data from four countries (Canada, Germany, the United Kingdom, and the United States) were included in the analysis, comprising 1,842 children diagnosed with leukemia and 3,099 controls (diagnosis dates ranged from 1988 to 1996). The odds ratios for nighttime ELF EMF exposure for categories of 0.1-<0.2 microT, 0.2-<0.4 microT, and >or=0.4 microT as compared with <0.1 microT were 1.11 (95% confidence interval (CI): 0.91, 1.36), 1.37 (95% CI: 0.99, 1.90), and 1.93 (95% CI: 1.11, 3.35), respectively. The fact that these estimates were similar to those derived using 24-/48-hour geometric mean values (odds ratios of 1.09, 1.20, and 1.98, respectively) indicates that the nighttime component cannot, on its own, account for the pattern observed. These results do not support the hypotheses that nighttime measures are more appropriate; hence, the observed association between ELF EMF and childhood leukemia still lacks a plausible explanation.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.017 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".