Aflatoxin B1 exposure during pregnancy and neonatal outcomes: A systematic review
Bibliographic record
Abstract
Background Evidence has suggested that exposure to aflatoxin B1 (AFB1) during pregnancy may be associated with adverse neonatal outcomes; however, available data are inconclusive. We conducted this systematic review to estimate the relation of AFB1 exposure during pregnancy to neonatal outcomes.Methods Scopus and PubMed databases were systematically searched for relevant publications published before December 2022 evaluating the connection between AFB1 exposure in pregnant women and outcomes such as small for gestational age (SGA), birth length, low birth weight (LBW), birth weight, and preterm birth (PTB).Results This study comprised 7 studies with a combined sample size of 4,047 participants. After pooling all available effect sizes, it was determined that there was no significant correlation between increased AFB1 exposure during pregnancy and SGA, PTB, LBW, birth weight, and birth length. Significant heterogeneity was observed across studies for LBW (I2 = 68.6%, p = 0.02), birth weight (I2 = 96.8%, p ≤ 0.001), and birth length (I2= 93.1%, p ≤ 0.001). However, the sensitivity analysis suggested that exposure to AFB1 might be significantly linked to higher odds of LBW infants and inversely related to both birth weight and birth length.Conclusion Exposure to AFB1 in pregnant women might be linked to adverse neonatal outcomes. Given the heterogeneity and the limited number of studies available, further high-quality, standardized research is essential to confirm or refute these findings with greater confidence.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".