Characteristics and conviction rates of injured alcohol-impaired drivers admitted to a tertiary care Canadian Trauma Centre
Bibliographic record
Abstract
PURPOSE: Alcohol intoxication is an important factor in motor vehicle crash (MVC) related morbidity and mortality. Despite greater societal attention, medical admission after MVC results in avoidance of legal consequences. We sought to determine characteristics of, and consequences to, injured alcohol-impaired drivers (IAIDs). METHODS: All injured adults [Injury Severity Score (ISS) >12, age>18] entered in a Trauma Centre registry between April 1 1995 to March 31 2003 were reviewed. Legally intoxicated patients who had been drivers involved in a MVC and who had a blood alcohol content (BAC) > or =80 mg/dl were cross-referenced to municipal and federal databases to identify investigations, charges, and legal outcomes. RESULTS: Of BACs obtained from 1933 (41%) of 4727 patients; 39% (757) were legally intoxicated (BAC > or =80 mg/dl); 185 (24%) were IAIDs. The IAIDs were generally very intoxicated (mean BAC 190 mg/dl); seriously injured (median ISS 22); often in ICU (47%), and had 8% mortality. Charges were laid against 69 (37%) of IAIDs, only 58 (31%) suffered legal consequences; 27 (15%) of impaired driving, and 31 (17%) of other convictions. All IAIDs who caused a fatal injury to another were convicted. A lower severity of injury of the IAIDs, non-fatal injury to another, and occurrence in the more recent years of the study were independently associated with a conviction in multivariable analysis. CONCLUSION: Despite increasing convictions over time and among most of those charged, the majority of injured drivers escape legal consequences. Increased BAC testing and reporting of this phenomenon could address this.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".