Factors influencing relapse during a 2-year follow-up of first-episode psychosis in a specialized early intervention service
Bibliographic record
Abstract
BACKGROUND: Differential association of risk factors associated with relapse following treatment of first-episode psychosis (FEP) have not been studied adequately, especially for patients treated in specialized early intervention (SEI) services, where some of the usual risk factors may be ameliorated. METHOD: Consecutive FEP patients treated in an SEI service over a 4-year period were evaluated for relapse during a 2-year follow-up. Relapse was based on ratings on the Scale for Assessment of Positive Symptoms (SAPS) and weekly ratings based on the Life Chart Schedule (LCS). Predictor variables included gender, duration of untreated psychosis (DUP), total duration of untreated illness (DUI), age of onset, pre-morbid adjustment, co-morbid diagnosis of substance abuse during follow-up and adherence to medication. Univariate analyses were followed by logistic regression for rate of relapse and survival analysis with the Cox proportional-hazards regression model for time to relapse as the dependent variables. RESULTS: Of the 189 eligible patients, 145 achieved remission of positive symptoms. A high rate of medication adherence (85%) and relatively low relapse rates (29.7%) were observed over the 2-year follow-up. A higher relapse rate was associated with a co-morbid diagnosis of substance abuse assessed during the follow-up period [odds ratio (OR) 2.84, 95% confidence interval (CI) 1.24-6.51]. The length of time to relapse was not associated with any single predictor. CONCLUSIONS: Specialized treatment of substance abuse may be necessary to further reduce risk of relapse even after improving adherence to medication.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".