Association between Small for Gestational Age and Attention-deficit/Hyperactivity Disorder: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: This study aimed to conduct a meta-analysis to assess whether neonates born small for gestational age (SGA) have an increased risk of developing Attention-deficit/hyperactivity disorder (ADHD). Methods: In order to identify relevant studies examining the association between neonates born SGA and ADHD in children, we conducted comprehensive searches across major databases, including PubMed, Scopus, and Web of Science. The meta-analysis employed a random-effects model, and the Newcastle-Ottawa scale (NOS) was used to assess the quality of the included studies. Results: A total of 13 records, comprising a sample population of 12,610,162, were included. The pooled estimates of Relative Risk (RR) and Odds Ratio (OR) showed a significant association between SGA neonates and ADHD in children, with RR = 1.35 (95% CI: 1.20, 1.50) and OR = 1.29 (95% CI: 1.03, 1.55). While no significant association was found between SGA and the risk of ADHD in casecontrol studies (OR = 1.25, 95% CI: 0.77, 1.73, p=0.000, I2=97.0%), a significant association was observed in cohort studies (OR = 1.25, 95% CI: 1.19, 1.31, p=0.665, I2=0.0%). Cohort study results demonstrated homogeneity (I2=0.0%). Discussion: This novel meta-analysis reveals a significant association between neonates born SGA and an increased risk of ADHD. Although considerable heterogeneity was observed, subgroup analysis confirmed SGA as a discernible risk factor. Conclusion: The present meta-analysis showed that being born small for gestational age (SGA) is a risk factor for developing attentiondeficit/ hyperactivity disorder (ADHD).
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".