Benefits of Enriched Intervention Compared with Standard Care for Patients with Recent-Onset Psychosis: A Metaanalytic Approach
Bibliographic record
Abstract
OBJECTIVE: To assess the effectiveness of enriched intervention (EI) on symptomatic and functional outcomes, compared with standard care (SC). METHOD: Studies were retrieved from search engines and, using a metaanalytic approach, we compared El trials with SC trials. Eleven EI sample trials (1053 patients) and 6 SC sample trials (500 patients), totalling data from 1553 patients (69% male), were examined. We calculated the effect sizes (ESs) of both symptomatic and functional improvement over a follow-up period of about 1 year. RESULTS: Significant differences between El and SC were observed at follow-up for the improvement of both positive and negative symptoms, respectively: positive, EI = -1.54 (95%CI, -1.63 to -1.45 ) and SC = -1.07 (95%CI, -1.19 to -0.94) (Qbetween = 40.3, df 1, P < 0.001); negative, EI= -0.44 (95%CI, -0.53 to -0.35) and SC = -0.18 (95%Cl, -0.31 to -0.05) (Qbetween = 10.6, df 1, P < 0.01). We also observed a significant difference between the El and the SC groups for functional improvement over the follow-up period with mean EI = 1.11 (95%CI, 0.99 to 1.23) and SC = 0.63 (95%CI, 0.49 to 0.77) (Qbetween = 24.5, df 1, P < 0.001). CONCLUSIONS: There is now quantitative evidence across multiple studies and sites to indicate that Els for patients with recent-onset psychosis are significantly more effective than SC for symptomatic and functional improvement over a period of about 1 year.
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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 | 0.033 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".