Accuracy of Three Screening Instruments in Identifying Preschool Children at Risk for Autism Spectrum Disorder
Bibliographic record
Abstract
An efficient approach for screening and identifying children at risk for autism spectrum disorder (ASD) remains a pressing need. The aim of this exploratory study was to examine the ability of two general developmental screening tests to identify children at risk for ASD. We compared the accuracy of one general developmental screening instrument, Ages and Stages Questionnaires (ASQ), and one general social emotional screening instrument, the Ages and Stages Questionnaire: Social Emotional (ASQ:SE), with the Social Communication Questionnaire (SCQ), an ASD-specific screening instrument. Two hundred eight children between 36 and 66 months were recruited through 19 community ASD programs, websites, and magazines. The three screening instruments were given to 285 parent/child dyads with and without a diagnosis of ASD, online via a screening website linked to a university. Sixty-four children had been diagnosed with ASD and were receiving special education services (e.g., behavioral interventions) prior to their participation. The classification agreement of the ASQ (i.e., sensitivity = 84.38%, specificity = 81.45%) outperformed the other two screening instruments; classification agreement of the SCQ was sensitivity = 70.31% and specificity = 87.33%; and of the ASQ: SE, sensitivity = 82.81% and specificity = 72.40%. Agreement among the questionnaires ranged from moderate to strong as measured by Pearson Product Moment Correlation Coefficients. Children diagnosed with ASD had scores below the screening cutoff points, indicating risk, most often on three ASQ domains: (a) communication, (b) gross motor, and (c) personal social. This exploratory study indicated the feasibility of using the ASQ in screening clinics for finding children at risk for ASD, if the ASQ is followed by specific ASD assessments. Design limitations, including a sample of children with ASD already receiving intervention services may explain the somewhat lower sensitivity of the SCQ.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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".