Early Life Nutrition Factors and Risk of Acute Leukemia in Children: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Acute leukemia commonly occurs in young children with peak incidence at the age of 2-5 years. However, the etiology is still unclear and many preventable risk factors still deserve to be reviewed. The focus of this systematic review and meta-analysis is to summarize the evidence concerning early life nourishment (breastfeeding, early life diet), neonatal vitamin K administration and the risk of acute leukemia. All epidemiological studies published up to June 2023 and assessing diet-related risk factors for childhood acute leukemia were identified in two electronic databases (PubMed and Web of Science), with no limits on publication year or language. A total of 38 studies (37 case-control studies and 1 study with pooled analysis) were included. The published risk estimates were combined into a meta-analysis using the Generic Inverse Variance method. The current evidence shows that breastfeeding (yes vs. no) has a protective effect against acute lymphoblastic leukemia (odds ratio = 0.85; 95% CI, 0.76-0.94). Evidence related to the role of other studied factors (foods and supplements) is inconclusive. Further research into the potential role of diet in early life and the risk of acute leukemia is needed to develop prevention strategies at population level. Review Registration: PROSPERO registration no. CRD42019128937.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".