Relationship Between Relapse and Hospitalization in First-Episode Psychosis
Bibliographic record
Abstract
OBJECTIVE: Relapse is a frequently used outcome measure in schizophrenia research. However, difficulties in reliably measuring relapse diminish its usefulness. Hospitalization is a potential alternative, but its relationship to relapse has not been assessed. METHODS: This study used data from a two-year, prospective study to examine associations between relapse and hospitalization in a cohort of 200 Canadian patients with first-episode psychosis. First, the relationship between relapse and hospitalization was assessed by cross-tabulating relapse and hospitalization. Next, survival curves of time to first relapse or hospitalization were compared. Finally, to examine the convergent validity of relapse and hospitalization, the predictive capacity of three predictors were examined: a substance use disorder diagnosis, prior hospitalization, and medication adherence. RESULTS: Rates of both relapse and hospitalization were similar. During the two-year follow-up, 37% of the patients experienced a relapse, and 26% were hospitalized. As an indicator of relapse, hospitalization had a low sensitivity (47%) and high specificity (87%). A higher risk of hospitalization and relapse was associated with prior hospitalization, a substance use disorder diagnosis, and medication nonadherence. CONCLUSIONS: Results indicated that relapse and hospitalization are separate but related outcome measures. They had similar frequencies and were found to have similar relationships with some predictors. Relapse is a more useful outcome measure in smaller clinical studies in which routine standardized clinical measures can be used. Hospitalization is more relevant in larger studies or as a quality indicator for studies using administrative databases, and it serves as a good measure for quality management in health systems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".