Behavioral measures of drinking: patterns from the Alcohol Interlock Record
Bibliographic record
Abstract
AIMS: This report reviews breath test data captured by the alcohol ignition interlock, a device that prevents a car from starting when BAC (breath alcohol concentration) is elevated. DESIGN: The predictors were elevated BAC test rates from ignition interlock equipped cars of traffic offenders convicted of DUI (driving under the influence of alcohol) and who used interlocks for 6-18 months. Outcome data were future DUI convictions. SETTINGS: Québec and Alberta, Canada. PARTICIPANTS: Approximately 10000 interlock users from these two culturally distinctive English- and French-speaking Provinces. MEASUREMENT: Predictor patterns were analyzed from among 23 million breath tests. Repeat DUI convictions accumulated up to several years after interlock removal were studied as an outcome to be predicted by the rate of BAC tests > or = 20 mg/dl (0.02%) while the interlock was installed. Data were analyzed with sensitivity and survival methods. FINDINGS: A median of eight interlock breath tests per day per driver were logged (a rate of 3000 tests/year). Less than 1% of all tests were over 0.02%, but the rate of elevated BAC tests, particularly those taken at 7-9 a.m., strongly predicts repeat DUI offenses 2 years hence. The interlock record is an unobtrusive measure of drinking behavior and can be used to profile driver risk. CONCLUSIONS: With new legal mandates, North American use of these DUI control devices is increasing rapidly from the current 5% penetration rate. Interlock data may eventually come to serve as a useful adjunct for patient monitoring by alcohol counselors as well as by courts and motor vehicle authorities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".