Advancing understanding of the classification of broad autism phenotype and attention‐deficit/hyperactivity disorder symptom dimensions within the Hierarchical Taxonomy of Psychopathology
Bibliographic record
Abstract
Research on personality and psychopathology associations has informed the classification of many symptom dimensions within the Hierarchical Taxonomy of Psychopathology (HiTOP). However, classification of symptom dimensions defining autism and attention-deficit/hyperactivity disorder (ADHD) within the HiTOP framework remains unclear in many ways. To address this issue, we examined the joint factor structure of (a) measures assessing characteristics relevant to ADHD and autism and (b) normal range personality traits in a sample of 547 adults recruited from Amazon Mechanical Turk, many of whom reported elevated autism-relevant and ADHD-relevant characteristics. We also examined how factors identified in these analyses correlated with measures of internalizing symptoms and select externalizing traits. Our results indicated that some measures assessing autism-relevant and ADHD-relevant characteristics (e.g. communication issues, hyperactivity/impulsivity) defined a distinct Attention and Communication Difficulties factor, with scores on this factor correlating strongly with internalizing symptom ratings. However, other relevant characteristics such as aloofness may be indicators of existing HiTOP spectra such as detachment. We discuss how these findings inform classification of autism-relevant and ADHD-relevant characteristics within the HiTOP, as well as key future directions for extending the limited research in this area.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".