Clinical utility of the at-risk for psychosis state beyond transition: A multidimensional network analysis
Bibliographic record
Abstract
To be relevant to healthcare systems, the clinical high risk for psychosis (CHR-P) concept should denote a specific (i.e., unique) clinical population and provide useful information to guide the choice of intervention. The current study applied network analyses to examine the clinical specificities of CHR-P youths compared to general help-seekers and non-CHR-P youth. 146 CHR-P (mean age = 14.32 years) and 103 non-CHR-P (mean age = 12.58 years) help-seeking youth were recruited from a neuropsychiatric unit and assessed using the Structured Interview for Psychosis-Risk Syndromes, Children's Depression Inventory, Multidimensional Anxiety Scale for Children, Global Functioning: Social, Global Functioning: Role, and Wechsler Intelligence Scale for Children/Wechsler Adult Intelligence Scale. The first network structure comprised the entire help-seeking sample (i.e., help-seekers network), the second only CHR-P patients (i.e., CHR-P network), and the third only non-CHR-P patients (i.e., non-CHR-P network). In the help-seekers network, each variable presented at least one edge. In the CHR-P network, two isolated "archipelagos of symptoms" were identified: (a) a subgraph including functioning, anxiety, depressive, negative, disorganization, and general symptoms; and (b) a subgraph including positive symptoms and the intelligence quotient. In the non-CHR-P network, positive symptoms were negatively connected to functioning, disorganization, and negative symptoms. Positive symptoms were less connected in the CHR-P network, indicating a need for specific interventions alongside those treating comorbid disorders. The findings suggest specific clinical characteristics of CHR-P youth to guide the development of tailored interventions, thereby supporting the clinical utility of the CHR-P concept.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".