Developing a Risk-Model of Time to First-Relapse for Children and Adolescents With a Psychotic Disorder
Bibliographic record
Abstract
Individuals treated for psychotic disorders and mood disorders with psychotic features have a high likelihood of relapse across the life course. This study examines the relapse rate and its associated predictors for children and adolescents experiencing a first-episode and develops a statistical risk-model for prediction of time to first-relapse. A multiyear, retrospective cohort design was used to track youth, under the age of 18 years, who experienced a first-episode of psychosis, and were admitted to 1 of 6 inpatient hospital psychiatric units (N = 87). Participants were followed for at least 2 years (M = 3.9, SD = 1.3) using survival analysis. Approximately 60% of subjects experienced relapse requiring hospital readmission by the end of follow-up, with 33% readmitted within the first year and 44% within 2 years. Median survival time was 34 months. Cox proportional hazards regression identified 4 key risk factors for relapse: medication nonadherence, female gender, receiving clinical treatment, and a decline in social support before first admission.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".