Diagnostic accuracy of Ages and Stages Questionnaire, Third Edition to identify abnormal or delayed gross motor development in high‐risk infants
Bibliographic record
Abstract
AIM: To investigate the diagnostic accuracy of parent-completed Ages and Stages Questionnaire, Third Edition (ASQ-3) to identify abnormal or delayed gross motor development in infants born less than 1000 g or less than 28 weeks gestation. METHODS: Prospective cohort study of high-risk infants comparing ASQ-3 as the index test with concurrent score on Alberta Infant Motor Scale (AIMS) as the reference standard, at 4-, 8- and 12-month corrected (post-term) age. Reference standard positivity cut-offs were 'Abnormal motor development' (AIMS Clinical Range) and 'Motor delay' (AIMS score >1 SD below mean, not captured in Clinical Range). RESULTS: Participating infants (n = 191) had mean gestational age (95% confidence interval (CI)) 26.8 weeks (26.6-27.1) and mean birthweight (95% CI) 870 g (844-896). AIMS rated 51%, 31% and 23% of infants as having 'Abnormal motor development' and 12%, 28% and 13% with 'Motor delay', at 4, 8 and 12 months, respectively. Diagnostic accuracy of ASQ-3 to identify abnormal motor development was acceptable for older infants only if 'Monitor' cut-off was used: sensitivity (95% CI) 33% (23-44), 86% (73-95) and 80% (63-92) and specificity (95% CI) 84% (74-92), 76% (66-84), and 76% (67-83) at 4, 8 and 12 months, respectively. ASQ-3 sensitivity to identify motor delay was low. CONCLUSIONS: ASQ-3 has poor sensitivity to identify abnormal or delayed motor development at 4 months. Using the 'Monitor' cut-off improves the diagnostic accuracy of ASQ-3 for identification of older infants with abnormal motor development who are at high risk of motor disability. However, ASQ-3 has poor sensitivity to identify motor delay. Clinical motor assessment of high-risk infants is recommended, particularly in early infancy.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".