Impact of Exposure to Antibiotics During Pregnancy and Infancy on Childhood Obesity: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate whether antibiotic exposure during pregnancy and infancy was associated with childhood overweight or obesity. METHODS: PubMed, Embase, and Cochrane Library databases were searched from the inception date to April 18, 2019, to identify observational studies that investigated the association between antibiotic exposure during pregnancy and infancy and childhood overweight or obesity. After study selection and data extraction, the meta-analysis was conducted using Stata software version 12.0 (StataCorp LP, College Station, Texas). The evaluation of the methodological quality was carried out by AMSTAR 2 (Bruyère Research Institute, Ottawa, Ontario, Canada). RESULTS: A total of 23 observational studies involving 1,253,035 participants were included. The meta-analysis showed that prenatal exposure to antibiotics was not significantly associated with childhood overweight or obesity, whereas an increased risk of overweight or obesity was seen in subgroup analysis of the second trimester (risk ratio = 1.13; 95% CI: 1.06-1.22; P = 0.001). In contrast, antibiotic exposure during infancy could increase the risk of childhood overweight or obesity (risk ratio = 1.14; 95% CI: 1.06-1.23; P = 0.001). CONCLUSIONS: This meta-analysis found that antibiotic exposure during the second trimester and infancy could increase the risk of childhood overweight or obesity.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.006 | 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 | 0.002 |
| 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".