Static and Dynamic Assessment of Violence Risk Among Discharged Forensic Patients
Bibliographic record
Abstract
This study evaluated the predictive validity of structured instruments for violent recidivism among a sample of 82 patients discharged from a maximum security forensic psychiatric hospital. The incremental predictive validity of dynamic pre–post change scores was also assessed. Each of the Historical-Clinical-Risk Management-20 Version 3 (HCR-20 V3 ), Psychopathy Checklist–Revised, Short-Term Assessment of Risk and Treatability, Violence Risk Scale (VRS), and Violence Risk Appraisal Guide–Revised was rated based on institutional files. The study instruments significantly predicted community-based violent recidivism (area under the curve [AUC] = 0.68-0.85), even after controlling for time at risk using Cox regression survival analyses. Dynamic change scores computed from the HCR-20 V3 Relevance ratings and from the VRS also demonstrated incremental predictive validity, controlling for baseline scores. The findings provided support for the use of the study instruments to assess violence risk and for the consideration of dynamic changes in risk—provided that valid means of assessment are employed.
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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".