Screening for developmental delay at 18 months using the Infant Toddler Checklist: A validation study
Bibliographic record
Abstract
OBJECTIVE: The Infant Toddler Checklist (ITC) may be promising as a single tool at the 18-month visit to detect a range of developmental concerns. We examined the predictive validity of the ITC; and the association between positive ITC screening and health care utilization (HCU). METHODS: Prospective cohort study of children at average-risk for developmental delay attending their 18-month visit in primary care in Toronto, Canada. Parents completed the ITC. HCU from the single-payer provincial health system was collected from health administrative databases ensuring complete follow-up. Physician billing code for a neurodevelopmental consultation was the primary outcome and criterion measure. Six other HCU types were assessed. RESULTS: Of 1460 children with a mean age at screening of 18 months, 11% screened ITC positive. Mean age at follow-up was 8 years, 2.6% had a neurodevelopmental consultation. Screening test properties (with neurodevelopmental consultation as the criterion measure): 40% sensitivity (95% CI 24%, 57%), 90% specificity (95% CI 88%, 91%), 10% false positive rate (95% CI 9%, 12%). Using multivariable negative binomial regression, a positive ITC was associated with higher rates of 6 of 7 HCU types, including neurodevelopmental consultation (aRR 2.78, 95% CI 1.37, 5.67, p = 0.005). CONCLUSION: The ITC had high specificity and a low false positive rate, suggesting that most children with a negative ITC will not have a later neurodevelopmental consultation, and use of the tool may minimize unintended harms such as anxiety and resource use. The low sensitivity highlights the importance of ongoing developmental surveillance. Low sensitivity of other screening tools is discussed.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".