SALIVARY CORTISOL: A PREDICTOR OF CONVICTIONS FOR DRIVING UNDER THE INFLUENCE OF ALCOHOL?
Bibliographic record
Abstract
AIMS: To examine the relationship between salivary cortisol and frequency of past driving under the influence of alcohol (DUI) convictions. METHODS: A total of 104 males with previous DUI convictions (from one to eight) and mean age of 44.7 years were assessed on measures characterizing repeat DUI offenders, including sociodemographic information, alcohol use behaviours, biological indices of the organic consequences of chronic abuse, negative consequences of excessive drinking, past DUI conviction history, impulse control, and antisocial behaviour tendencies. Saliva samples were taken approximately every 30 min over a 6 h period during an exhaustive multidimensional assessment protocol, and were then assayed to obtain cortisol responses. RESULTS: Blunted cortisol response, typically observed in alcoholics and in high-risk non-alcoholics, was associated with increased number of past DUI convictions. This association was particularly pronounced in multiple DUI offenders, and was stronger than, and independent of, other measures of alcohol use severity and chronicity commonly used for DUI assessment. CONCLUSIONS: Cortisol response may be useful in understanding the mediators underlying repeat DUI offending and the frequent failure of intervention efforts in curbing DUI behaviour.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".