Short duration of untreated psychosis enhances negative symptom remission in extended early intervention service for psychosis
Bibliographic record
Abstract
OBJECTIVE: To test whether duration of untreated psychosis (DUP) < 3 months, recommended by the World Health Organization/International Early Psychosis Association, enhances the effects of an extended early intervention service (EEIS) on symptom remission. METHOD: We examined data from a randomized controlled trial in which patients who received 2 years of treatment in EIS for psychosis were subsequently randomized to either 3 years of EEIS or 3 years of regular care (RC). Using a DUP cut-off ≤ 12 weeks (approximately < 3 months), patients were split into two groups. Length of positive, negative and total symptom remission were the outcomes. RESULTS: Patients (N = 217) were mostly male (68%) with schizophrenia spectrum disorder (65%); 108 (50%) received EEIS (58 had DUP ≤12 weeks; 50 had DUP >12 weeks). Interaction between treatment condition (EEIS vs. RC) and DUP cut-off ≤ 12 weeks was only significant in multiple linear regression model examining length of negative symptom remission as the outcome (adjusted β = 36.88 [SE = 15.88], t = 2.32, P = 0.02). EEIS patients with DUP ≤12 weeks achieved 25 more weeks of negative symptom remission than EEIS patients with DUP >12 weeks. CONCLUSION: Having a short DUP may be critical in deriving long-term benefits from EIS for psychosis, including EEIS settings. This work empirically supports policy recommendations of reducing DUP <3 months.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".