ArticleLaw and human behavior2026
Dynamic prediction of reoffending in individuals given community sentences: Development and validation of a novel risk monitoring assessment tool (oxMore).
Article in Law and human behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveThis study aimed to develop and validate a dynamic risk assessment tool for individuals serving community sentences that accounts for the effects of acute adverse events and desistance from crime. HYPOTHESES: Dynamic risk prediction models that incorporate updated data on mental health relapses, incidents of victimization, and desistance from crime will produce more accurate risk stratification for reoffending than models lacking dynamic measurement of risk factors.
methodWe analyzed a national cohort of 59,676 individuals given community sentences in Sweden, of whom 23,879 (45%) had prior psychiatric diagnoses and 18,546 (31%) had substance use disorder diagnoses. Model development tested prespecified criminal history, sociodemographic, and clinical risk factors. Employing landmarking methods for time-to-event data, we modeled the effects of new health care episodes during community supervision, changes in a supervised individual's circumstances, and the impact of crime desistance. We validated the model in a geographically distinct population.
resultsDuring follow up, 18,307 (31%) were reconvicted, 4,416 (7%) committed a violent offense, and 5,381 (9%) were hospitalized with a psychiatric diagnosis. The model demonstrated strong calibration and discrimination performance (c-index = 0.74 for violent reoffending, c-index = 0.69 for general reoffending). It also outperformed comparison models that did not incorporate dynamic data. The final model was translated into an online risk calculator (OxMore).
conclusionsImplementation of dynamic models could lead to more accurate risk stratification for individuals under community supervision, including those with psychiatric and substance use disorders, potentially improving resource allocation, and linkage to interventions that reduce recidivism rates. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.