The diagnostic boundary between autism spectrum disorder, intellectual developmental disorder and schizophrenia spectrum disorders
Bibliographic record
Abstract
Purpose – During the last few years the prevalence of autism and Autism Spectrum Disorder (ASD) has increased greatly. A recurring issue is the overlap and boundaries between Intellectual Developmental Disorder (IDD), ASD and Schizophrenia Spectrum Disorders (SSD). In clinical practice with people with IDD, the alternative or adjunctive diagnosis of ASD or SSD is particularly challenging. The purpose of this paper is to define the boundaries and overlapping clinical characteristics of IDD, ASD and SSD; highlight the most relevant differences in clinical presentation; and provide a clinical framework within which to recognize the impact of IDD and ASD in the diagnosis of SSD. Design/methodology/approach – A systematic mapping of the international literature was conducted on the basis of the following questions: first, what are considered to be core and overlapping aspects of IDD, ASD and SSD; second, what are the main issues in clinical practice; and third, can key diagnostic flags be identified to assist in differentiating between the three diagnostic categories? Findings – Crucial clinical aspects for the differentiation resulted to be age of onset, interest towards others, main positive symptoms, and anatomical anomalies of the central nervous system. More robust diagnostic criteria and semeiological references are desirable. Originality/value – The present literature mapping provides a comprehensive description of the most relevant differences in the clinical presentation of ASD and SSD in persons with IDD.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".