RIPTOSO: The development of a screening tool for adverse events during forensic-psychiatric inpatient treatments of offenders with schizophrenia spectrum disorders
Bibliographic record
Abstract
Adverse events such as compulsory measures, absconding, illicit substance use, self-harm, aggressive behavior, and prolonged hospitalization pose significant challenges in forensic psychiatric inpatient care. This study introduces a machine learning-based tool to predict these events in patients with schizophrenia spectrum disorders (SSD) upon admission. Data from 370 court-mandated forensic inpatients treated at an academic center in Zurich, Switzerland, were retrospectively analyzed. Twenty-seven variables, available upon admission in clinical settings, were tested using six machine learning algorithms (support vector machines (SVM), logistic regression, naive Bayes, gradient boosting, fine trees, and neural networks). Predictive performance was assessed using metrics such as area under the curve (AUC) and balanced accuracy. SVM demonstrated the highest performance, achieving an AUC of 0.79 and a balanced accuracy of 69.8 %. These results suggest that the tool can identify patients at higher risk for problematic treatment courses, enabling earlier interventions and more efficient resource allocation. The simplicity of the model, based on routinely collected data, enhances its clinical applicability. However, validation studies in multi-center and international settings are essential to confirm its robustness and generalizability. This tool represents a promising step toward integrating machine learning into forensic psychiatry to improve treatment outcomes and patient safety.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".