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Record W2045598993 · doi:10.1080/1538-950391915740

The Alcohol Interlock: An Underutilized Resource for Predicting and Controlling Drunk Drivers

2003· article· en· W2045598993 on OpenAlexaboutno aff
Paul R. Marques, A. Scott Tippetts, Robert B. Voas

Bibliographic record

VenueTraffic Injury Prevention · 2003
Typearticle
Languageen
FieldEngineering
TopicAutonomous Vehicle Technology and Safety
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsDrunk drivingInterlockDrunk driversPoison controlOccupational safety and healthInjury preventionHuman factors and ergonomicsResource (disambiguation)AlcoholDriving under the influenceEngineeringSuicide preventionTransport engineeringBusinessForensic engineeringComputer securityEnvironmental healthMedical emergencyMedicineComputer scienceChemistry

Abstract

fetched live from OpenAlex

This report summarizes evidence presented during the Third Annual Ignition Interlock Symposium at Vero Beach, Florida, 29 October 2002. The ignition interlock prevents a car from starting when blood alcohol concentration (BAC) is elevated. We review some of our prior work as well as introduce previously unpublished results to demonstrate the manner in which the data recorded by the alcohol ignition interlock device can serve as an advance predictor of future driving under the influence (DUI) of alcohol risks. Data used in this current report represent approximately 2,200 ignition interlock users from Alberta, Canada, and about 8,000 interlock users from Quebec, Canada; the Alberta data set contained 5.5 million breath tests and the Quebec data 18.8 million breath tests. All tests are time and date stamped and this information was used to characterize patterns of BAC and vehicle use, and the relationship between BAC elevations and DUI offenses that accumulated after the interlock was removed from the vehicles. Findings from Cox regression (Marques et al., 2003) show that BAC elevations > .02-.04% are more potent predictors of repeat DUI (p < .0001) than even prior DUI (p < .006), usually found to be the strongest indicator of driver risk. Prior DUI obviously has no use for scaling the risk of first-time offenders. Drivers who are both multiple offenders and who have more than a few elevated interlock BAC tests are much more likely to repeat DUI. The timing and pattern of elevated BAC tests provided during the time drivers were required to use an alcohol ignition interlock device are remarkably similar on both a daily basis and an hourly basis when the interlock programs from the two provinces are compared directly. Both provinces had higher rates of elevated tests on Saturday and Sunday, and the fewest elevated tests on Tuesdays. The absolute rate of elevated tests is similar despite the two provinces adhering to different interlock lockout points (.02% Quebec; .04% Alberta). Charts tracking the Monday-Friday timing of elevated BAC tests by hour are nearly identical for both provinces. The most elevated BAC tests occurred between 7 and 9 A.M. Monday to Friday, even though most vehicle start attempts occurred much later in the day. This higher rate of elevated morning BAC likely represents drinking from the prior evening with alcohol not yet cleared from circulation; those with elevated BAC in the early morning were more likely to have a repeat offense even after accounting for prior DUI and the higher overall rate of elevated BAC tests. This is viewed as evidence of a drinking problem that will lead to impaired driving after the controlling function of the interlock is removed. Policy changes are discussed that might take better advantage of interlock information to improve the public response to drunk driving.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.528

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.011
GPT teacher head0.249
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2003
Admission routes1
Has abstractyes

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