Predicting first-episode psychosis patients who will never relapse over 10 years
Bibliographic record
Abstract
BACKGROUND: Although relapse in psychosis is common, a small proportion of patients will not relapse in the long term. We examined the proportion and predictors of patients who never relapsed in the 10 years following complete resolution of positive symptoms from their first psychotic episode. METHOD: Patients who previously enrolled in a 12-month randomized controlled trial on medication discontinuation and relapse following first-episode psychosis (FEP) were followed up after 10 years. Relapse of positive symptoms was operationalized as a change from a Clinical Global Impression scale positive score of <3 for at least 3 consecutive months to a score of ⩾3 (mild or more severe). Baseline predictors included basic demographics, premorbid functioning, symptoms, functioning, and neurocognitive functioning. RESULTS: Out of 178 first-episode patients, 37 (21%) never relapsed during the 10-year period. Univariate predictors (p ⩽ 0.1) of patients who never relapsed included a duration of untreated psychosis (DUP) ⩽30 days, diagnosed with non-schizophrenia spectrum disorders, having less severe negative symptoms, and performing better in logical memory immediate recall and verbal fluency tests. A multivariate logistic regression analysis further suggested that the absence of any relapsing episodes was significantly related to better short-term verbal memory, shorter DUP, and non-schizophrenia spectrum disorders. CONCLUSIONS: Treatment delay and neurocognitive function are potentially modifiable predictors of good long-term prognosis in FEP. These predictors are informative as they can be incorporated into an optimum risk prediction model in the future, which would help with clinical decision making regarding maintenance treatment in FEP.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".