Prenatal antibiotic use and risk of childhood wheeze/asthma: A meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Existing body of knowledge suggests that antibiotic use during pregnancy was inconsistently associated with childhood wheeze/asthma. The aim of this study was to determine whether exposure to antibiotic during pregnancy could increase the risk for childhood wheeze/asthma using a comprehensive meta-analysis. METHODS: PubMed, MEDLINE, and China National Knowledge Infrastructure (CNKI) were systematically searched for studies up to September 10, 2014, and additional studies were found by searching reference lists of relevant articles. For this meta-analysis, cohort studies and case-control studies assessing the association between antibiotic use during pregnancy and risk of childhood wheeze/asthma were included. Extracted data were mainly pooled using random-effects model. Study quality was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). RESULTS: Ten studies were identified in final analysis. Pooling analysis of these studies showed an OR of 1.20 (95% CI, 1.13-1.27) for wheeze/asthma. After excluding case-control studies and prospective studies without achieving high scores on the NOS, the pooled OR was 1.18 (95% CI, 1.11-1.26). We found the risk of antibiotic use and pooled ORs of wheeze/asthma were 1.09 (95% CI, 0.92-1.29) for the first trimester, 1.14 (95% CI, 1.01-1.29) for the second trimester, and 1.33 (95% CI, 1.11-1.60) for the third trimester, respectively. CONCLUSIONS: This meta-analysis suggests that antibiotic exposure during pregnancy may increase the risk of wheeze/asthma in childhood. Besides, the risk of developing wheeze/asthma in childhood was marked during last two trimesters of pregnancy. Future studies of large-size and prospective cohorts which adequately address concerns for confounder bias are needed to examine the relationship between antibiotic use and risk of childhood asthma.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.068 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".