The association between gestational age at delivery and neonatal abstinence syndrome: A systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: Some evidence suggests that infants born at later gestational age (GA) are at higher risk of developing neonatal abstinence syndrome (NAS). This systematic review estimated the association between GA at delivery and development of NAS in infants born to women on opioid agonist therapy (OAT). Methods: MEDLINE/PubMed, Scopus, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials were searched from January 2000 to April 2023. Studies reporting data on the association between GA and NAS among pregnant women being treated with OAT were eligible for inclusion. Random effects meta-analysis was used to estimate the mean difference in GA between infants affected by NAS and unaffected infants; odds ratio (OR) for the association between preterm birth and NAS; and OR for the association between gestational week and NAS. Results: Of 966 records identified, 38 studies were eligible for this review. The pooled mean difference in GA between infants affected by NAS and unaffected infants was 0.62 weeks (95% CI: 0.08–1.16, I2=90.7%). The odds of developing NAS were estimated to increase by 3% per gestational week (OR 1.03, 95% CI: 0.997-1.06, I2=84.2%). The OR for the association between preterm birth and developing NAS was estimated to be 0.87 (95% CI: 0.63-1.21, I2=85.7%). Conclusions: The data included in this review demonstrate that higher GA is unlikely to be associated with an increased risk of NAS, although poor study quality and significant study heterogeneity were observed.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".