Predictive Properties of the Violence Risk Scale–Sexual Offense Version as a Function of Age
Bibliographic record
Abstract
The present study examined the discrimination and calibration properties of Violence Risk Scale-Sexual Offense version (VRS-SO) risk and change scores for sexual and violent recidivism as a function of age at release, on a combined sample of 1,287 men who had attended sexual offense-specific treatment services. The key aim was to examine to what extent VRS-SO scores can accurately discriminate recidivists from nonrecidivists among older cohorts, and if the existing age-related adjustments in the instrument adequately correct for increasing age. VRS-SO risk and change scores showed consistent properties of discrimination for sexual recidivism across the age cohorts, via area under the curve and Cox regression survival analysis, as demonstrated through fixed effects meta-analysis. Calibration analyses, employing logistic regression, demonstrated that age at release was consistently incrementally predictive of violent, but not sexual, recidivism after controlling for individual differences on static and dynamic risk factors. E/O index analyses demonstrated that predicted rates of sexual recidivism from VRS-SO scores, particularly when employed with Static-99R, were not significantly different from those observed among age cohorts; however, calibration was weaker for general violence. Implications for use of the VRS-SO in sexual recidivism risk assessment with older offenders are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".