Nonchromosomal birth defects and risk of childhood acute leukemia: An assessment in 15 000 leukemia cases and 46 000 controls from the Childhood Cancer and Leukemia International Consortium
Bibliographic record
Abstract
Although recent studies have demonstrated associations between nonchromosomal birth defects and several pediatric cancers, less is known about their role on childhood leukemia susceptibility. Using data from the Childhood Cancer and Leukemia International Consortium, we evaluated associations between nonchromosomal birth defects and childhood leukemia. Pooling consortium data from 18 questionnaire-based and three registry-based case-control studies across 13 countries, we used multivariable logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between a spectrum of birth defects and leukemia. Our analyses included acute lymphoblastic leukemia (ALL, n = 13 115) and acute myeloid leukemia (AML, n = 2120) cases, along with 46 172 controls. We used the false discovery rate to account for multiple comparisons. In the questionnaire-based studies, the prevalence of birth defects was 5% among cases vs 4% in controls, whereas, in the registry-based studies, the prevalence was 11% among cases vs 7% in controls. In pooled adjusted analyses, there were several notable associations, including (1) digestive system defects and ALL (OR = 2.70, 95% CI: 1.46-4.98); (2) congenital anomalies of the heart and circulatory system and AML (OR = 2.86, 95% CI: 1.81-4.52) and (3) nervous system defects and AML (OR = 4.23, 95% CI: 1.50-11.89). Effect sizes were generally larger in registry-based studies. Overall, our results could point to novel genetic and environmental factors associated with birth defects that could also increase leukemia susceptibility. Additionally, differences between questionnaire- and registry-based studies point to the importance of complementary sources of birth defect phenotype data when exploring these associations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".