Protocol for the development and testing of the schiZotypy Autism Questionnaire (ZAQ) in adults: a new screening tool to discriminate autism spectrum disorder from schizotypal disorder
Bibliographic record
Abstract
BACKGROUND: Autism spectrum disorder (ASD) and schizotypal disorder (SD) both have a heterogenous presentation, with significant overlaps in symptoms and behaviour. Due to elevated recognition and knowledge of ASD worldwide, there is a growing rate of referrals from primary health professionals to specialised units. At all levels of assessment, the differential diagnostic considerations between ASD and SD exert major challenges for clinicians. Although several validated screening questionnaires exist for ASD and SD, none have differential diagnostic properties. Accordingly, in this study, we aim to develop a new screening questionnaire, the schiZotypy Autism Questionnaire (ZAQ), which provides a combined screening for both conditions, while also indicating the relative likelihood of each. METHODS: We aim to test 200 autistic patients and 100 schizotypy patients recruited from specialised psychiatric clinics and 200 controls from the general population (Phase 1). The results from ZAQ will be compared to the clinical diagnoses from interdisciplinary teams at specialised psychiatric clinics. After this initial testing phase, the ZAQ will be validated in an independent sample (Phase 2). CONCLUSIONS: The aim of the study is to investigate the discriminative properties (ASD vs. SD), diagnostic accuracy, and validity of the schiZotypy Autism Questionnaire (ZAQ). FUNDING: Funding was provided by Psychiatric Centre Glostrup, Copenhagen Denmark, Sofiefonden (Grant number: FID4107425), Trygfonden (Grant number:153588), Takeda Pharma. TRIAL REGISTRATION: Clinical Trials, NCT05213286, Registered 28 January 2022, clinicaltrials.gov/ct2/show/NCT05213286?cond = RAADS&draw = 2&rank = 1.
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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 | 0.034 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.032 |
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".