Drinking Water Contaminants and Childhood Leukemia
Bibliographic record
Abstract
We conducted a population-based case-control study to evaluate the relation between exposure to drinking water contaminants (total and specific trihalomethanes and certain metals and nitrates) and childhood acute lymphoblastic leukemia. We compared 491 cases 0-9 years of age with 491 controls. We developed a municipality-exposure matrix based on municipal and provincial historical data, a tapwater survey in 227 homes, and information about residential history. We used average level of exposure and cumulative average over the period as exposure indices, and we measured risk for the pregnancy period as well as for the postnatal period. We show that risks were generally not increased for the prenatal period nor with average levels of exposure. Postnatal cumulative exposure for total trihalomethanes at above the 95th percentile of the distribution for cases and controls was associated with an odds ratio of 1.54 (95% confidence interval = 0.78-3.03); for that same period, risk associated with exposure to chloroform was increased (odds ratio = 1.63; 95% confidence interval = 0.84-3.19) as well as that for exposure to zinc (odds ratio = 2.48; 95% confidence interval = 0.99-6.24). Risks were also increased for exposure to cadmium and arsenic, but not for other metals nor for nitrates.
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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 | 0.001 | 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".