Psychometric properties of the Mandarin version of the autism diagnostic observation Schedule-Generic
Bibliographic record
Abstract
BACKGROUND/PURPOSE: The diagnosis of autism spectrum disorder (ASD), involving multiple components of clinical assessments, is challenging. The Autism Diagnostic Observation Schedule-Generic (ADOS-G), one of the standardized and validated instruments for ASD diagnostic evaluation, has been widely used in many countries. With the preparation of the Mandarin version of the ADOS-G (Mandarin-ADOS-G), this study aims to examine its psychometric properties, including reliability and validity. METHODS: The sample included 554 individuals clinically diagnosed with ASD (477 males, 86.1%) and 50 typically developing (TD) individuals (29 males, 58.0%) who were assessed with different modules of the Mandarin-ADOS-G between 4.1 and 34.0 years old with a mean age of 13.0 years (Module 1, n = 40; Module 2, n = 46; Module 3, n = 275; Module 4, n = 243). We evaluated the inter-rater reliability, test-retest reliability, internal consistency, and concurrent validity with the Chinese Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) caregiver-report and self-report forms. The discriminative validity of Mandarin-ADOS-G was also examined. RESULTS: The Mandarin-ADOS-G demonstrated good inter-rater reliability (agreement of ADOS classification 0.91), good test-retest reliability (intraclass correlations 0.55-0.73), and low to high good internal consistency (Cronbach's alpha 0.27-0.86). The concurrent validity showed significant correlations with ADI-R (Pearson correlations 0.22-0.37) and the SRS caregiver-report form (Pearson correlations 0.15-0.23). Moreover, all Mandarin-ADOS-G domains successfully differentiated autistic individuals from TD individuals (all p-values <0.001). CONCLUSION: The Mandarin-ADOS-G is a reliable and valid instrument for assisting the diagnosis of ASD in the Mandarin-speaking population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".