Relationship Between Relapse and Hospitalization in First-Episode Psychosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it