Paternal Occupational Exposure to Endocrine-Disrupting Chemicals as a Risk Factor for Leukaemia in Children: A Case-Control Study from the North of England
Bibliographic record
Abstract
Occupations with exposures to a variety of chemicals, including those thought to be potential endocrine disruptors, have been associated with an increased risk of leukaemia in offspring. We investigated whether an association exists between paternal occupations at birth involving such exposures and risk of leukaemia in offspring. Cases ( n=958 ) were matched, on sex and year of birth, to controls from two independent sources, one other cancers, one cancer-free live births. Paternal occupations at birth were classified, using an occupational exposure matrix, as having “very unlikely,” “possible,” or “likely” exposure to six groups of potential endocrine-disrupting chemicals. There was a significantly increased risk of acute nonlymphocytic leukaemia (ANLL) for polychlorinated organic compounds (OR 1.95, 95% CI 1.08–3.54) only in comparison with cancer-free controls, and for phthalates (OR 1.61, 95% CI 1.00–2.61) only with registry controls. A number of other, including inverse, associations were seen, but limited to one control group only. No associations were seen with likely paternal exposure to heavy metals. The associations identified in this study require further investigation, with better exposure and potential confounding (for example maternal variables) information, to evaluate the likelihood of true associations to assess whether they are real or due to chance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".