Influence of the combination of alcohol and benzodiazepines on driving / by Hillary G. Maxwell.
Bibliographic record
Abstract
Although the increased risk associated with driving under the influence of alcohol or \nbenzodiazepines on their own has been recognized, several variables make their \ncombined effects difficult to study. As a result, the small body of research on the subject \nis contradictory. The current study aimed to further explore the effects of the \ncombination of alcohol and benzodiazepines on driving. Data from the years 1993 - \n2006 were taken from the American Fatality Analysis Reporting System and examined \nusing a case control design. All subjects were drivers, aged 20 years and older, had been \ntested for alcohol and drugs, and, if positive for benzodiazepines, were only positive for a \nsingle half-life class of benzodiazepine. Cases had at least one unsafe driver action (e.g., \nweaving) recorded in relation to the crash. Controls had no such record. Logistic \nregression was performed to determine the odds ofperforming an unsafe driver action \n(UDA) for drivers positive for benzodiazepines (stratified by short, intermediate and long \nhalf-life) with BACs ranging from 0.00 to 0.10 mg/100 ml. When compared to an \nalcohol- and benzodiazepine-free referent group, the alcohol plus benzodiazepine groups \nshowed significantly higher odds of committing an UDA at nearly every BAC / half-life \ncombination. When using the alcohol only and benzodiazepine only groups as referents, \nadditive, possibly synergistic effects were observed for long benzodiazepines in \ncombination with alcohol at BACs of 0.02 and 0.04 mg/100 ml. This study demonstrates \nthe detrimental effects that the combination of alcohol and benzodiazepines can have on \ndriving, and suggests that further research is necessary.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".