Incidence and risk factors for autism spectrum disorder among infants born <29 weeks’ gestation
Bibliographic record
Abstract
Objective: This study was aimed to assess the incidence of and risk factors for autism spectrum disorder (ASD) among preterm infants born <29 weeks' gestational age (GA). Methods: A retrospective cohort study of infants born <29 weeks' GA admitted to two tertiary neonatal intensive care units (2009 to 2017) and followed ≥18 months corrected age (CA) at a neonatal follow-up clinic. The primary outcome was ASD, diagnosed using standardized testing or provisional diagnosis at ≥18 months CA. Patient data and 18-month CA developmental outcomes were obtained from the local Canadian Neonatal Follow Up Network database and chart review. Stepwise logistic regression assessed factors associated with ASD. Results: Among 300 eligible infants, 26 (8.7%) were diagnosed with confirmed and 21 (7.0%) with provisional ASD for a combined incidence of 15.7% (95% confidence interval [CI] 11.7 to 20.3). The mean follow-up duration was 3.9 ± 1.4 years and the mean age of diagnosis was 3.7 ± 1.5 years. Male sex (adjusted odds ratio [aOR] 4.63, 95% CI 2.12 to 10.10), small for gestational age status (aOR 3.03, 95% CI 1.02 to 9.01), maternal age ≥35 years at delivery (aOR 2.22, 95% CI 1.08 to 4.57) and smoking during pregnancy (aOR 5.67, 95% CI 1.86 to 17.29) were significantly associated with ASD. Among ASD infants with a complete 18-month CA developmental assessment, 46% (19/41) had no neurodevelopmental impairment (Bayley-III<70, deafness, blindness, or cerebral palsy). Conclusions: ASD is common among infants born <29 weeks' GA and possibly associated with identified risk factors. Such findings emphasize the importance of ASD evaluation among infants <29 weeks' GA and for continued reporting of developmental outcomes beyond 18-months of corrected age.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".