Specificity of Incident Diagnostic Outcomes in Patients at Clinical High Risk for Psychosis
Bibliographic record
Abstract
It is not well established whether the incident outcomes of the clinical high-risk (CHR) syndrome for psychosis are diagnostically specific for psychosis or whether CHR patients also are at elevated risk for a variety of nonpsychotic disorders. We collected 2 samples (NAPLS-1, PREDICT) that contained CHR patients and a control group who responded to CHR recruitment efforts but did not meet CHR criteria on interview (help-seeking comparison patients [HSC]). Incident diagnostic outcomes were defined as the occurrence of a SIPS-defined psychosis or a structured interview diagnosis from 1 of 3 nonpsychotic Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) groups (anxiety, bipolar, or nonbipolar mood disorder), when no diagnosis in that group was present at baseline. Logistic regression revealed that the CHR vs HSC effect did not vary significantly across study for any emergent diagnostic outcome; data from the 2 studies were therefore combined. CHR (n = 271) vs HSC (n = 171) emergent outcomes were: psychosis 19.6% vs 1.8%, bipolar disorders 1.1% vs 1.2%, nonbipolar mood disorders 4.4% vs 5.3%, and anxiety disorders 5.2% vs 5.3%. The main effect of CHR vs HSC was statistically significant (OR = 13.8, 95% CI 4.2-45.0, df = 1, P < .001) for emergent psychosis but not for any emergent nonpsychotic disorder. Sensitivity analyses confirmed these findings. Within the CHR group emergent psychosis was significantly more likely than each nonpsychotic DSM-IV emergent disorder, and within the HSC group emergent psychosis was significantly less likely than most emergent nonpsychotic disorders. The CHR syndrome is specific as a marker for research on predictors and mechanisms of developing 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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".