Relapse rates in an early psychosis treatment service
Bibliographic record
Abstract
UNLABELLED: To examine the relapse rate achievable in a real-life early psychosis treatment service. METHOD: A 2-year longitudinal cohort study of consecutive admissions to an early psychosis programme which served the entire population in a catchment area. The primary outcome measure was relapse. RESULTS: One hundred and forty-eight consecutive consenting admissions were recruited, 124 (83.8%) of whom were followed for 1 year and 116 (78.4%) for 2 years. Relapse was assessed by clinicians using structured criteria. The 2-year relapse rate among subjects with complete data collection was 34/95 (35.7%, 95% CI 26.2-46.3). A Kaplan-Meier life table censoring subjects lost to follow-up yielded a comparable estimate of the proportion not relapsing: 68% (95% CI 58-76%). CONCLUSION: These estimates compare favourably with a published range of 2-year relapse from 55% to 70% in the older literature and are comparable with the results in recent clinical trials.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".