Role of the perinatal experience on the risk of acute leukemia in childhood or adolescence: Systematic review and meta‐analysis
Bibliographic record
Abstract
Acute leukemia is the most common type of cancer in children; however, the etiology is poorly understood. The objective of this review was to summarize the current evidence of the role of perinatal factors in the development of acute leukemia. All epidemiological studies published up to October 2023 that evaluated perinatal risk factors for childhood acute leukemia were identified using a multi-tiered approach in two electronic databases (PubMed and Web of Science), without restriction on publication year or language. A total of 85 studies (13 prospective cohort studies, 62 case-control studies, and 10 pooled analyses) were included. We combined the published risk estimates in a meta-analysis, using the Generic Inverse Variance method. An increased risk of acute leukemia and the lymphoblastic subtype (ALL) was associated with high birth weight (>4000 g) (odds ratio [OR] = 1.35; 95% confidence interval [95% CI] 1.20-1.53 and OR = 1.21; 95% CI 1.08-1.34, respectively), maternal history of abortion (OR = 1.27; 95% CI 1.12-1.43 and OR = 1.24; 95% CI 1.08-1.43, respectively), and maternal diabetes (OR = 1.30; 95% CI 1.14-1.48 and OR = 1.32; 95% CI 1.16-1.50, respectively). In addition, an increased risk for ALL was also associated with maternal hypertension (OR = 1.21; 95% CI 1.06-1.38) and cesarean section (OR = 1.10; 95% CI 1.05-1.16). Our review suggests a potential role for perinatal factors in the development of acute leukemia in children. These findings indicate potential avenues for developing cost-effective prevention strategies applicable at the population level, while the mechanism of action is investigated.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".