Autism Spectrum Screening Checklist (ASSC): The Development of a Scale to Identify High-Risk Individuals Within the Children's Mental Health System
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a complex childhood onset neurodevelopmental disorder that has become the fastest growing developmental disability. Due to the increased demand for diagnostic assessments and subsequent increased wait times, standardized screening as part of regular clinical practice is needed. More specifically, there is an important need for the development of a more streamlined screening tool within an existing assessment system to identify those at greatest risk of having ASD. The current study utilized data from ~17,000 assessments obtained within the province of Ontario, based on the interRAI Child and Youth Mental Health (ChYMH) and Child and Youth Mental Health and Developmental Disability (ChYMH-DD), to develop a scale to identify children who have a higher likelihood of having autism. The scale was then tested on a trial population with data from the interRAI Early Years instrument. Further analyses examined the predictive validity of the scale. The Autism Spectrum Screening Checklist (ASSC) was found to be a good predictor of ASD with a sensitivity of 0.73 and specificity of 0.62, at the recommended cut-point of 2+. The results were consistent across several age ranges, specifically from 2 to 21 years of age. The ASSC scale provides an initial screen to help identify children and youth at heightened risk for autism within larger populations being assessed as part of routine practice. The main goal for the development and implementation of the ASSC scale is to harness the power of the existing interRAI assessment system to provide a more efficient, effective screening and referral process. This will ultimately help improve patient outcomes through needs-based care.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".