At Clinical High Risk for Psychosis: Outcome for Nonconverters
Bibliographic record
Abstract
OBJECTIVE: A major focus of early intervention research is determining the risk of conversion to psychosis and developing optimal algorithms of prediction. Although reported rates of nonconversion vary in the literature, the nonconversion rate always encompasses a majority (50%-85%) of the sample participants. Less is known about the outcome among this group, referred to as false positive individuals. METHOD: A longitudinal study was conducted of more than 300 prospectively identified treatment-seeking individuals meeting criteria for a psychosis-risk syndrome. Participants were recruited and evaluated across eight clinical research centers as part of the North American Prodrome Longitudinal Study. Over a 2.5-year follow-up assessment period, 214 (71%) participants had not made the transition to psychosis. RESULTS: The sample examined included 111 individuals who had at least 1 year of follow-up data available and did not transition to psychosis within the study duration. In year 1, there was significant improvement in ratings for attenuated positive and negative symptoms. However, at least one attenuated positive symptom was still present for 43% of the sample at 1 year and for 41% at 2 years. At the follow-up timepoints, social and role functioning were significantly poorer in the clinical sample relative to nonpsychiatric comparison subjects. CONCLUSIONS: Help-seeking individuals who meet prodromal criteria appear to represent those who are truly at risk for psychosis and are showing the first signs of illness, those who remit in terms of the symptoms used to index clinical high-risk status, and those who continue to have attenuated positive symptoms.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".