Predicting reoffense in pedophilic child molesters by clinical diagnoses and risk assessment.
Bibliographic record
Abstract
A Diagnostic and Statistical Manual of Mental Disorders (DSM)-based diagnosis of pedophilia has so far failed to predict sexual reoffense in convicted child molesters, probably because of its broad and unspecific conceptualization. In this study, therefore, we investigated the prognostic value of the subtype exclusive pedophilia and a series of customary risk assessment instruments (SSPI, Static-99, Stable-2007, VRS:SO) and the PCL-R in a sample of prison released pedophilic sexual offenders. First, we examined the convergent validity of risk assessment instruments (N = 261). Then, we calculated the predictive accuracy of the measures and diagnosis for sexual recidivism by ROC analyses and subsequent Cox regression (N = 189). Also, predictive values with more clinical immediacy were calculated (sensitivity, specificity, PPV and NPV). The VRS:SO, the SSPI, and the Static-99 significantly predicted sexual recidivism, as did a diagnosis of exclusive pedophilia. Also, the VRS:SO predicted sexual reoffense significantly better than the Stable-2007, the Static-99/Stable-2007 combined score, and the PCL-R. When used combined, only the VRS:SO and a diagnosis of exclusive pedophilia added incremental validity to each other. Our findings support that the clinical diagnosis of an exclusive pedophilia based on DSM criteria and VRS:SO defined risk factors can reliably discriminate higher from lower risk offenders, even within the select subgroup of pedophilic child molesters.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".