The predictive value of neurobiological measures for recidivism in delinquent male young adults
Bibliographic record
Abstract
Background: Neurobiological measures have been associated with delinquent behaviour, but little is known about the predictive power of these measures for criminal recidivism and whether they have incremental value over and above demographic and behavioural measures. This study examined whether selected measures of autonomic functioning, functional neuroimaging and electroencephalography predict overall and serious recidivism in a sample of 127 delinquent young adults. Methods: We assessed demographics; education and intelligence; previous delinquency and drug use; behavioural traits, including aggression and psychopathy; and neurobiological measures, including heart rate, heart rate variability, functional brain activity during an inhibition task and 2 electroencephalographic measures of error-processing. We tested longitudinal associations with recidivism using Cox proportional hazard models and predictive power using C-indexes. Results: Past offences, long-term cannabis use and reactive aggression were strongly associated with recidivism, as were resting heart rate and error-processing. In the predictive model, demographics, past delinquency, drug use and behavioural traits had moderate predictive power for overall and for serious recidivism (C-index over 30 months [fraction of pairs in the data, where the higher observed survival time was correctly predicted]: C30 = 0.68 and 0.75, respectively). Neurobiological measures significantly improved predictive power (C30 = 0.72 for overall recidivism and C30 = 0.80 for serious recidivism). Limitations: Findings cannot be generalized to females, and follow-up was limited to 4 years. Conclusion: Demographic and behavioural characteristics longitudinally predicted recidivism in delinquent male young adults, and neurobiological measures improved the models. This led to good predictive function, particularly for serious recidivism. Importantly, the most feasible measures (autonomic functioning and electroencephalography) proved to be useful neurobiological predictors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".