Predictors of Remission and Recovery in a First-Episode Schizophrenia Spectrum Disorder Sample: 2-Year Follow-up of the OPUS Trial
Bibliographic record
Abstract
OBJECTIVE: To examine the frequency and predictors of good outcome for patients with first-episode schizophrenia spectrum disorder (SSD). METHOD: We conducted a 2-year follow-up of a cohort of patients (n = 547) with first-episode SSD. We evaluated the patients on demographic variables, diagnosis, duration of untreated psychosis (DUP), premorbid functioning, psychotic and negative symptoms, substance abuse, adherence to medication, and service use. ORs were calculated with logistic regression analyses. RESULTS: A total of 369 patients (67%) participated in the follow-up interview. After 2 years, 36% remitted and 17% were considered fully recovered. Full recovery was associated with shorter DUP, better premorbid adjustment, fewer negative symptoms at baseline, no substance abuse at baseline, and adherence to medication and OPUS treatment. CONCLUSIONS: Several predictive factors were identified, and focus should be on potentially malleable predictors of outcome, for example, reducing DUP and paying special attention to patients who are unlikely to achieve good outcome, for example, patients with a substance abuse problem and poor premorbid adjustment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".