Prospective Associations Between Infant Sleep at 12 Months and Autism Spectrum Disorder Screening Scores at 24 Months in a Community-Based Birth Cohort
Bibliographic record
Abstract
BACKGROUND: Sleep problems have been associated with autism spectrum disorder (ASD) symptoms and diagnosis. However, past research has studied the simultaneous association of sleep problems with precursor ASD symptoms. Using data from a birth cohort, we estimate prospective associations between infant sleep characteristics at 12 months and later ASD screening scores at 24 months. METHODS: We obtained data from children (N = 1,096) and their mothers as participants in the Conditions Affecting Neurocognitive Development and Learning in Early Childhood longitudinal birth cohort study. Mothers were enrolled between 2006 and 2011, when they were 16-26 weeks pregnant. Using linear regression, we examined the influence of infant sleep characteristics (nighttime and daytime sleep, night wakings, and sleep onset latency) at 12 months on ASD screening scores at 24 months while controlling for other psychosocial characteristics. RESULTS: The number of night wakings was the only sleep characteristic at 12 months to be significantly associated with the development of early ASD symptoms at 24 months (B = 0.097, P = .021; 95% CI, 0.014 to 0.180). However, other competing risks, especially child socioemotional competence at 12 months (B = 0.573, P < .001; 95% CI, 0.361 to 0.785), showed stronger relative contributions in predicting ASD risk. CONCLUSIONS: Infants with more sleep problems by 12 months, especially those waking more often during the night, showed an increased number of early ASD symptoms a year later. This study suggests that infant sleep characteristics could constitute one clinical sign of ASD risk, together with key psychosocial characteristics.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".