Estimating driver risk using alcohol biomarkers, interlock blood alcohol concentration tests and psychometric assessments: initial descriptives
Bibliographic record
Abstract
AIM: To identify alcohol biomarker and psychometric measures that relate to drivers' blood alcohol concentration (BAC) patterns from ignition interlock devices (IIDs). DESIGN, SETTING, PARTICIPANTS, MEASUREMENTS: In Alberta, Canada, 534 drivers, convicted of driving under the influence of alcohol (DUI), installed IIDs and agreed to participate in a research study. IID BAC tests are an established proxy for predicting future DUI convictions. Three risk groups were defined by rates of failed BAC tests. Program entry and follow-up blood samples (n = 302, 171) were used to measure phosphatidyl ethanol (PETH), carbohydrate deficient transferrin (%CDT), gamma glutamyltransferase (GGT) and other biomarkers. Program entry urine (n = 130) was analyzed for ethyl glucuronide (ETG) and ethyl sulphate (ETS). Entry hair samples were tested for fatty acid ethyl esters (FAEE) (n = 92) and ETG (n = 146). Psychometric measures included the DSM-4 Diagnostic Interview Schedule Alcohol Module, Alcohol Use Disorders Identification Test (AUDIT), the time-line follow-back (TLFB), the Drinker Inventory of Consequences (DRINC) and the Temptation and Restraint Inventory (TRI). FINDINGS: Except for FAEE, all alcohol biomarkers were related significantly to the interlock BAC test profiles; higher marker levels predicted higher rates of interlock BAC test failures. PETH, the strongest with an overall analysis of variance F ratio of 35.5, had significant correlations with all nine of the other alcohol biomarkers and with 16 of 19 psychometric variables. Urine ETG and ETS were correlated strongly with the IID BAC tests. CONCLUSIONS: The findings suggest that several alcohol biomarkers and assessments could play an important role in the prediction and control of driver alcohol risk when re-licensing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".