The association between iron supplementation during pregnancy and the risk of childhood leukemia: a meta-analysis of case-control studies
Bibliographic record
Abstract
Acute leukemia (AL) presents significant health challenges, particularly in children, and iron plays a critical role in cellular processes that could influence cancer development. The study was motivated by the need to clarify the potential role of iron supplementation during pregnancy in influencing the risk of developing childhood leukemia. This meta-analysis adhered to PRISMA guidelines and systematically searched PubMed, Scopus, and Web of Science databases up to April 2024 for relevant observational studies. Inclusion criteria focused on case-control studies assessing the relationship between iron supplementation during pregnancy and leukemia risk, reporting odds ratios (ORs) with 95% confidence intervals (CIs). Data extraction and quality assessment were performed independently by two researchers using the Newcastle-Ottawa Scale (NOS). Statistical analysis involved calculating overall relative risk (RR) using a random-effects model and assessing heterogeneity through Cochran’s Q test and the I2 statistic. Publication bias was evaluated using Egger’s and Begg’s tests. The study analyzed data from 9 studies with 12 data sets involving a total of 4281 participants (2327 cases and 1954 controls). The findings indicated no significant association between iron supplementation during pregnancy and the overall risk of childhood leukemia (OR:1.01; 95% CI: 0.84–1.21, I2 = 63.2%). Also, no relationship was found between receiving iron supplements during pregnancy and the risk of AML (OR:1.01; 95% CI: 0.84–1.21, I2 = 56.6%) and ALL (OR:1.00; 95% CI: 0.81–1.24, I2 = 67.3%). This study found no significant association between iron supplementation during pregnancy and AL risk among case-control studies. Further research is needed to explore the potential influence of genetic and environmental factors on this relationship.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.047 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".