Using the ages and stages questionnaire in the general population as a measure for identifying children not at risk of a neurodevelopmental disorder
Bibliographic record
Abstract
BACKGROUND: Early detection of neurodevelopmental disorders (NDDs) enables access to early interventions for children. We assess the Ages and Stages Questionnaire (ASQ)'s ability to identify children with a NDD in population data. METHOD: Children 4 to 5 years old in the National Longitudinal Survey of Children and Youth (NLSCY) from cycles 5 to 8 were included. The sensitivity, specificity, positive and negative predictive values were calculated for the ASQ at 24, 27, 30, 33, 36 and 42 months. Fixed effects regression analyses assessed longitudinal associations between domain scores and child age. RESULTS: Specificity for the ASQ was high with 1SD or 2SD cutoffs, indicating good accuracy in detecting children who will not develop a NDD, however the sensitivity varied over time points and cut-offs. Sensitivity for the 1 SD cutoff at 24 months was above the recommended value of 70% for screening. Differences in ASQ domains scores between children with and without NDD increases with age. CONCLUSIONS: The high specificity and negative predictive values of the ASQ support its use in identifying children who are not at the risk of developing a NDD. The capacity of the ASQ to identify children with a NDD in the general population is limited except for the ASQ-24 months with 1SD and can be used to identify children at risk of NDD.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".