Predictive validity of the Infant Toddler Checklist in primary care at the 18-month visit and developmental diagnosis at 3–5 years: a prospective cohort study
Bibliographic record
Abstract
Objective There is international variation in recommendations regarding developmental screening and growing recognition of the low sensitivity of commonly used developmental screening tools. Our objective was to examine the predictive validity of the Infant Toddler Checklist (ITC) at 18 months to predict a developmental diagnosis at 3–5 years, in a primary care setting. Methods We designed a prospective cohort study, recruiting in primary care in Toronto, Canada. Parents completed the ITC at the 18-month visit and reported developmental diagnosis at 3–5 years (developmental delay, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), learning problem). We calculated screening test properties with 95% CIs. We used multivariable logistic regression analyses adjusted for important covariates. Results In the final sample (n=488), mean age at screening was 18.5 (SD 1.1) months, and at follow-up was 46.6 (SD 10.0) months. At screening, 46 (9.4%) had a positive ITC. At follow-up, 26 (5.3%) had a developmental diagnosis, including: developmental delay (n=22), ASD (n=4), ADHD (n=1), learning problem (n=1); parents of two children each reported two diagnoses (total of 28 diagnoses). Of four children with a diagnosis of ASD at follow-up, three had a positive ITC at 18 months. The ITC specificity (92%, 95% CI: 89% to 94%) and negative predictive value (96%, 95% CI: 95% to 97%) were high; false positive rate was low (8%, 95% CI: 6% to 11%); sensitivity was low (31%, 95% CI: 14% to 52%). There was a strong association between a positive ITC at 18 months and later developmental diagnosis (adjusted OR 4.48, 95% CI: 1.72 to 11.64; p=0.002). Conclusion The ITC had high specificity, high negative predictive value, low false positive rate, and identified children with later developmental delay and ASD. The ITC had low sensitivity, similar to other screening tools underscoring the importance of continuous developmental surveillance at all health supervision visits.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".