MEASUREMENT AND CLASSIFICATION OF THE HETEROGENEOUS AUTISM PHENOTYPE
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a heterogeneous disorder with a high burden of suffering and economic cost to society. The current Thesis represents a systematic attempt to investigate ASD heterogeneity, as it relates to the measurement and classification of the clinical phenotype. The Thesis integrates information from multiple constructs (symptoms, traits, behaviours), methods (factor analysis, cluster analysis, and factor mixture modeling), populations (clinical and high-risk samples) and time points (at diagnosis and at age 6) for the investigation of the underlying structure of the ASD phenotype in young children. The Thesis consists of four interrelated empirical studies and one Editorial. Results can be organized into three overarching themes: 1) in preschool children with ASD core diagnostic symptoms (social communication deficits and repetitive behaviours) appear to overlap with other emotional/behavioural problems (attention, withdrawal, anxiety, aggression, emotional reactivity); 2) along the heterogeneous autism spectrum there appear to be distinct, relatively homogeneous subgroups of children; on average, children across these subgroups differ in their levels of symptom severity, adaptive skills, and emotional/behavioural problems; 3) the underlying structure of the ASD symptom phenotype changes as children grow and develop. Thesis findings lend support to a much-needed shift in our conceptual and methodological approach to the study of measurement and classification of autism pathology: that is, instead of a set of categorical symptoms that present early in childhood and remain static over the life span, ASD might be better understood as a complex and dynamic disorder, structured on both categorical and dimensional constructs that vary not only across individuals at any given point, but also within individuals across time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".