A Meta-Analysis on the Impact of Prenatal and Early Childhood Antimicrobial Use on Autism Spectrum Disorders
Bibliographic record
Abstract
Objective: To investigate the impact of prenatal and early childhood antimicrobial use on autism spectrum disorders (ASD). Data Sources: We searched PubMed and Embase databases for relevant studies from inception to August 2022. Study Selection and Data Extraction: Peer-reviewed, observational studies were all acceptable. Raw data were extracted into a predefined worksheet and quality analysis was performed using the Newcastle–Ottawa Scale. Data Synthesis: Nineteen studies were identified in the meta-analysis. Prenatal antimicrobial exposure was not associated with ASD ( P = 0.06 > 0.05), whereas early childhood antimicrobial exposure was associated with an increased odds ratio of ASD (OR = 1.17, 95% CI = [1.08-1.27], P value < 0.001). The sibling-matched analysis, with a very limited sample size, suggested that neither prenatal ( P = 0.47 > 0.05) nor early childhood ( P = 0.13 > 0.05) antimicrobial exposure was associated with ASD. Medical professionals may need to take the possible association into consideration when prescribing an antimicrobial in children. Conclusions: Early childhood antimicrobial exposure could increase the incidence of ASD. In future studies, it would be necessary to control for confounding factors, such as genetic factors, parenteral age at birth, or low birthweight, to further validate the association.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.050 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".