Comparison of clinical outcomes following 2 years of treatment of first-episode psychosis in urban early intervention services in Canada and India
Bibliographic record
Abstract
BACKGROUND: Purported superior outcomes for treatment of psychosis in low- and middle-income (LMICs) compared with high-income (HICs) countries have not been examined in the context of early intervention services (EIS). AIMS: To compare 2-year clinical outcomes in first-episode psychosis (FEP) treated in EIS in Chennai (LMIC) and Montreal (HIC) using a similar EIS treatment protocol and to identify factors associated with any outcome differences. METHOD: Patients with FEP treated in EIS in Chennai (n = 168) and Montreal (n = 165) were compared on change in level of symptoms and rate and duration of positive and negative symptom remission over a 2-year period. Repeated-measures analysis of variance, and logistic and linear regression analyses were conducted. RESULTS: Four patients died in Chennai compared with none in Montreal. Family support was higher for Chennai patients (F = 14.05, d.f. = 1, P < 0.001, ƞp2 = 0.061) and increased over time at both sites (F = 7.0, d.f. = 1.915, P < 0.001, ƞp2 = 0.03). Negative symptom outcomes were significantly better in Chennai for level of symptoms (time × site interaction F = 7.36, d.f. = 1.49, P = 0.002, ƞp2 = 0.03), duration of remission (mean 16.1 v. 9.78 months, t = -7.35, d.f. = 331, P < 0.001, Cohen's d = 0.80) and the proportion of patients in remission (81.5% v. 60.3%, χ2 = 16.12, d.f. = 1, P < 0.001). The site differences in outcome remained robust after adjusting for inter-site differences in other characteristics. Early remission and family support facilitated better outcome on negative symptoms. No significant differences were observed in positive symptom outcomes. CONCLUSIONS: Patients with FEP treated in EIS in LMIC contexts are likely to show better outcome on negative symptoms compared with those in HIC contexts. Early remission and family support may benefit patients across both contexts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".