The Role of Cognition and Social Functioning as Predictors in the Transition to Psychosis for Youth With Attenuated Psychotic Symptoms
Bibliographic record
Abstract
In the literature, there have been several attempts to develop prediction models for youth who are at clinical high risk (CHR) of developing psychosis. Although there are no specific clinical or demographic variables that seem to consistently predict the later transition to psychosis in those CHR youth, in addition to attenuated psychotic symptoms, the most commonly occuring predictors tend to be poor social functioning and certain cognitive tasks. Unfortunately, there has been little attempt to replicate alogorithms. A recently published article by Cornblatt et al suggested that, for individuals with attentuated psychotic symptoms (APS), disorganized communication, suspiciousness, verbal memory, and a decline in social functioning were the best predictors of later transition to psychosis (the RAP model). The purpose of this article was to first test the prediction model of Cornblatt et al with a new sample of individuals with APS from the PREDICT study. The RAP model was not the best fit for the PREDICT data. However, using other variables from PREDICT, it was demonstrated that unusual thought content, disorganized communication, baseline social functioning, verbal fluency, and memory, processing speed and age were predictors of later transition to psychosis in the PREDICT sample. Although the predictors were different in these 2 models, both supported that disorganized communication, poor social functioning, and verbal memory, were good candidates as predictors for later conversion to psychosis.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".