Positive Screening for Autism in Ex-preterm Infants: Prevalence and Risk Factors
Bibliographic record
Abstract
OBJECTIVE: The survival of very low birth weight infants has increased markedly in recent years. Unfortunately, the prevalence of significant and lifelong motor, cognitive, and behavioral dysfunction has remained a major problem confronting these children. The objective of this study was to perform screening tests for early autistic features in children with a history of very low birth weight and to identify risk factors associated with a positive screening result. METHODS: We studied 91 ex-preterm infants < or = 1500 g at birth. Infants underwent conventional MRI studies at preterm and/or term-adjusted age. We collected pertinent demographic, prenatal, intrapartum, acute postnatal, and short-term outcome data for all infants. Follow-up assessments were performed at a mean age of 21.9 +/- 4.7 months, using the Modified Checklist for Autism in Toddlers, the Vineland Adaptive Behavior Scale, and the Child Behavior Checklist. RESULTS: Twenty-six percent of ex-preterm infants had a positive result on the autism screening tool. Abnormal scores correlated highly with internalizing behavioral problems on the Child Behavior Checklist and socialization and communication deficits on the Vineland Scales. Lower birth weight, gestational age, male gender, chorioamnionitis, acute intrapartum hemorrhage, illness severity on admission, and abnormal MRI studies were significantly associated with an abnormal autism screening score. CONCLUSIONS: Early autistic behaviors seem to be an underrecognized feature of very low birth weight infants. The results from this study suggest that early screening for signs of autism may be warranted in this high-risk population followed by definitive autism testing in those with positive screening results.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".