Home pesticide exposures and risk of childhood leukemia: Findings from the childhood leukemia international consortium
Bibliographic record
Abstract
Some previous studies have suggested that home pesticide exposure before birth and during a child's early years may increase the risk of childhood leukemia. To further investigate this, we pooled individual level data from 12 case-control studies in the Childhood Leukemia International Consortium. Exposure data were harmonized into compatible formats. Pooled analyses were undertaken using multivariable unconditional logistic regression. The odds ratio (ORs) for acute lymphoblastic leukemia (ALL) associated with any pesticide exposure shortly before conception, during pregnancy and after birth were 1.39 (95% confidence interval [CI]: 1.25, 1.55) (using 2,785 cases and 3,635 controls), 1.43 (95% CI: 1.32, 1.54) (5,055 cases and 7,370 controls) and 1.36 (95% CI: 1.23, 1.51) (4,162 cases and 5,179 controls), respectively. Corresponding ORs for risk of acute myeloid leukemia (AML) were 1.49 (95% CI: 1.02, 2.16) (173 cases and 1,789 controls), 1.55 (95% CI: 1.21, 1.99) (344 cases and 4,666 controls) and 1.08 (95% CI: 0.76, 1.53) (198 cases and 2,655 controls), respectively. There was little difference by type of pesticide used. The relative similarity in ORs between leukemia types, time periods and pesticide types may be explained by similar exposure patterns and effects across the time periods in ALL and AML, participants' exposure to multiple pesticides, or recall bias. Although some recall bias is likely, until a better study design can be found to investigate the associations between home pesticide use and childhood leukemia in an equally large sample, it would appear prudent to limit the use of home pesticides before and during pregnancy, and during childhood.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".