Prospective replication of the Violence Risk Appraisal Guide in predicting violent recidivism among forensic patients.
Bibliographic record
Abstract
An exhaustive survey of a cohort of forensic patients provided an opportunity for a prospective replication of the predictive accuracy of the Violence Risk Appraisal Guide (VRAG). Data collected during the original survey also permitted a test of the predictive accuracy of clinical assessments of risk on the same cohort. The VRAG yielded a large effect size in predicting violent recidivism (ROC area = .80) over a constant 5-year follow-up and performed significantly better than averaged clinical opinions. The superiority of the VRAG was also observed at very short follow-up times and for very serious violence. Moreover, for 16 subsamples, observed rates of violent recidivism did not differ significantly from the expected rates. VRAG score was unrelated, and clinical judgments inversely related to violent recidivism in the small low-risk sample of female forensic patients. The authors conclude that, regardless of length of opportunity or severity of outcome, actuarial methods are more accurate than is clinical judgment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".