Effectiveness of Enhanced Developmental Screening at 18 Months to Identify Developmental Delays
Bibliographic record
Abstract
OBJECTIVE: We sought to measure whether receipt of an enhanced 18-month well-baby visit with use of a developmental screening tool versus a routine 18-month well-baby visit (which typically involves developmental surveillance without screening) is associated with time to identification of developmental delays. METHOD: We conducted a cohort study of children (17-22 months) in Ontario who received an 18-month well-baby visit (March 2020‒March 2022), followed to September 2022 using linked health administrative datasets. Visits were categorized as enhanced (n = 83,554) or routine (n = 15,723). The outcome was the identification of a developmental delay within 6 months (early) and more than 6 months after (late) the 18-month visit. Piecewise Cox proportional hazards models estimated hazard ratios (aHR) adjusted for child, maternal, and physician factors, comparing developmental delay diagnosis by visit type. RESULTS: Children who received an enhanced visit were slightly older, had a lower representation in the most deprived group, and a higher percentage of patients with pediatricians as their usual provider of care. After adjustment, children with enhanced compared with routine visits were more likely to have developmental delays detected in the early period (aHR 1.19 95% CI 1.11‒1.28) but not in the late period following the well-baby visit. CONCLUSION: Enhanced visits are associated with earlier identification of developmental delays compared with routine visits in the 6 months following the 18-month well-baby visit. Enhanced developmental monitoring using screening tools may facilitate earlier recognition of developmental concerns.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".