Effectiveness of Community Treatment Order in Patients with a First Episode of Psychosis: A Mirror-Image Study
Bibliographic record
Abstract
OBJECTIVE: Poor adherence to antipsychotic medications is strongly associated with psychotic relapses and hospitalizations. This may hinder patients' ability to function, particularly in a first episode of psychosis (FEP). Poor adherence to treatment may be due to poor insight that can alter the capacity to consent to care, including pharmacotherapy. When patients are judged legally lacking the capacity to consent to care, treatment can be mandated through community treatment orders (CTOs). This naturalistic study examines the effects of CTOs in FEP patients. METHOD: This study examines 38 FEP patients legally deemed unable to consent to care during their follow-up. Using a naturalistic mirror-image approach, we compare clinical (Scale for the Assessment of Positive Symptoms [SAPS], Scale for the Assessment of Negative Symptoms [SANS]), functional (Global Assessment of Functioning Scale [GAF], Social and Occupational Functioning Assessment Scale [SOFAS]), and service use (number of emergency room visits, length of hospitalizations) indicators before and after CTO. RESULTS: After the CTO, 37 of 38 patients complied with treatment. Statistically significant improvements in clinical (▵SAPS = -6.3; 95% CI, 4.5 to 8.1 and ▵SANS = -2.2; 95% CI, 0.9 to 3.4, P < 0.01) and functional (▵GAF = +15.0; 95% CI, 8.4 to 21.6, ▵SOFAS = +18.6; 95% CI, 12.8 to 24.4, P < 0.01) outcomes were observed. Significant reduction in emergency room visits ( P = 0.016) and days of hospitalization per month in acute care units ( P < 0.05) were identified with no difference in hospital days per month in short-stay units. Moreover, encounters with case managers ( P = 0.008) and attendance of cognitive therapy sessions ( P = 0.031) were significantly higher. However, patients' weight significantly increased after CTO (▵weight = +8.0 kg, P < 0.01). CONCLUSIONS: In FEP patients, CTOs improve compliance to treatment, which contributes to reducing positive and negative symptoms, shortening hospital stays, and improving functioning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".