Corrected Age at Bayley Assessment and Developmental Delay in Extreme Preterms
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Research on outcomes of prematurity frequently examines neurodevelopment in the toddler years as an end point, but the age range at examination varies. We aimed to evaluate whether the corrected age (CA) at Bayley-III assessment is associated with rates of developmental delay in extremely preterm children. METHODS: This retrospective cohort study included children born at <29 weeks' gestation who were admitted in the Canadian Neonatal Network between 2009 and 2017. The primary outcomes were significant developmental delay (Bayley-III score <70 in any domain) and developmental delay (Bayley-III score <85 in any domain). To assess the association between CA at Bayley-III assessment and developmental delay, we compared outcomes between 2 groups of children: those assessed at 18 to 20 months' CA and 21-24 months. RESULTS: Overall, 3944 infants were assessed at 18-20 months' CA and 881 at 21-24 months. Compared with infants assessed at 18-20 months, those assessed at 21-24 months had higher odds of significant development delay (20.0% vs 12.5%; adjusted odds ratio, 1.75; 95% confidence interval [CI], 1.41-2.13) and development delays (48.9% vs 41.7%, adjusted odds ratio 1.33; 95% CI, 1.11-1.52). Bayley-III composite scores were on average 3 to 4 points lower in infants evaluated at 21-24 months' CA (for instance, adjusted mean difference and 95% CI for language: 3.49 [2.33-4.66]). Conversely, rates of cerebral palsy were comparable (4.6% vs 4.7%) between the groups. CONCLUSIONS: Bayley-III assessments performed at 21-24 months' CA were more likely to diagnose a significant developmental delay compared with 18- to 20-month assessments in extremely preterm children.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".