Maternal antibiotic exposure during pregnancy and the risk of allergic diseases in childhood: A meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Increasing studies suggest that antibiotic exposure during pregnancy may increase the risk of childhood allergic diseases; however, controversy still exists. Thus, we conducted this meta-analysis to evaluate the association between antibiotic use during pregnancy and childhood asthma/wheeze, eczema/atopic dermatitis, and food allergy. METHODS: CENTRAL, EMBASE, and PubMed were searched for studies up to July 20, 2020. Cohort studies and case-control studies that estimated the association of antibiotic exposure in pregnancy with the risk of childhood asthma/wheeze, eczema/atopic dermatitis, and food allergy were included. A random-effects model or fixed-effects model was used to calculate the pooled estimates. The quality of the included studies was assessed by the Newcastle-Ottawa Scale (NOS). Stata12.0 software was used to analyze the association through a meta-analysis. RESULTS: A total of 26 studies were included in the meta-analysis. The results showed that maternal antibiotic exposure in pregnancy and the summary OR for the risk of childhood asthma/wheeze was 1.29 (95% CI = 1.16-1.43), the summary OR for eczema/atopic dermatitis was 1.62 (95% CI = 1.16-2.27), and the pooled OR for food allergy was 1.36 (95% CI = 0.94-1.96). CONCLUSIONS: Our results indicated that maternal antibiotic use during pregnancy might increase the risk of asthma/wheeze and eczema/atopic dermatitis but not food allergy in children. Further studies with larger sample size and robust multivariable adjustment are needed to confirm our findings. Nevertheless, the appropriate use of antibiotics during pregnancy is incredibly important, and healthcare professionals should be selective when prescribing antibiotics for pregnant women.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.058 |
| Bibliometrics | 0.006 | 0.006 |
| 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".