Predictors of Admission in First-Episode Psychosis: Developing a Risk Adjustment Model for Service Comparisons
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to develop a risk adjustment model based on hospital admissions that would enable comparison between services for patients with a first episode of psychosis. METHODS: Candidate predictor variables for hospital admission were identified in a literature review, from which an expert panel selected 12 potential risk adjustment variables by using a structured process, the Template for Risk Adjustment Information Transfer. Multivariable logistic regression modeling with the 12 variables was used to develop models in one cohort of first-episode psychosis patients (N=297); these models were validated with data from a second cohort (N=309). The C statistic, a measure of model discrimination, was calculated to assess model performance. RESULTS: In the data from the development sample, prior hospitalization was the only significant predictor of hospital admissions within one year of enrollment in the first-episode psychosis program (odds ratio [OR]=1.88, p=.05). Hospital admissions after two and three years from admission to the program were significantly associated with higher levels of initial positive symptoms (OR=1.07, p=.02; OR=1.06, p=.02, respectively), and prior hospitalizations (OR=2.72, p=.001; OR=3.34, p<.001, respectively). The logistic models performed well, with C statistics ranging from .72 to .74 for the three outcomes, where a value of 1.0 implies perfect model discrimination. In the validation data the C statistics were slightly lower, ranging from .67 to .72. CONCLUSIONS: According to the C statistic estimates, the model developed provided good discrimination and was relatively robust in predicting hospitalization of first-episode psychosis patients.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".