Maternal prenatal infection and childhood leukaemia: a Childhood Cancer and Leukemia International Consortium (CLIC) meta-analysis
Bibliographic record
Abstract
BACKGROUND: Maternal prenatal infections may affect fetal development, increasing the immunological vulnerability of offspring to childhood leukaemia (CL). The role of maternal infections in CL is unclear and might vary by subtype (lymphoblastic, ALL; myeloid, AML) or other characteristics. Understanding this potentially modifiable risk factor could inform prevention strategies. METHODS: Seventeen hospital- and population-based case-control studies of children born in 1972-2019 within the Childhood Cancer and Leukemia International Consortium with self-questionnaires or health-registry data on maternal infection were included (13 638 cases; 26 870 controls). Meta-analyses assessed CL and maternal infection (overall, viral, bacterial, respiratory, influenza/cold, urinary, genital) stratified by subtype, infection timing, race and ethnicity, and diagnosis age. RESULTS: The adjusted meta-analysis odds ratio (OR) for any maternal prenatal infection was 1.13 [95% confidence interval (CI) 0.91-1.40], with similar estimates for ALL and AML. Infection-specific estimates varied. ORs for first-trimester infections were highest for CL and ALL, but not AML, although all CIs contained one. We found modest risk differences between White and Hispanic/Latino children, most notably for CL diagnosed at <2 years (White children: OR 1.25, 95% CI 1.02-1.53; Hispanic/Latino children: OR 0.79, 95% CI 0.34-1.81, subgroup difference P = .05), with similar differences for viral and respiratory/influenza/cold infections. CONCLUSION: Although findings only modestly support an association between maternal prenatal infections and CL, some infections might increase the risk more markedly in young White children compared with Hispanic/Latino children. Risk patterns across race and ethnicity, type, and timing of maternal prenatal infection merit further investigation, as do studies with documented exposure information.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.040 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".