Driving Under the Influence of Cannabis: Impact of Combining Toxicology Testing with Field Sobriety Tests
Bibliographic record
Abstract
BACKGROUND: Cannabis is increasingly used both medically and recreationally. With widespread use, there is growing concern about how to identify cannabis-impaired drivers. METHODS: A placebo-controlled randomized double-blinded protocol was conducted to study the effects of cannabis on driving performance. One hundred ninety-one participants were randomized to smoke ad libitum a cannabis cigarette containing placebo or delta-9-tetrahydrocannabinol (THC) (5.9% or 13.4%). Blood, oral fluid (OF), and breath samples were collected along with longitudinal driving performance on a simulator (standard deviation of lateral position [SDLP] and car following [coherence]) over a 5-hour period. Law enforcement officers performed field sobriety tests (FSTs) to determine if participants were impaired. RESULTS: There was no relationship between THC concentrations measured in blood, OF, or breath and SDLP or coherence at any of the timepoints studied (P > 0.05). FSTs were significant (P < 0.05) for classifying participants into the THC group vs the placebo group up to 188 minutes after smoking. Seventy-one minutes after smoking, FSTs classified 81% of the participants who received active drug as being impaired. However, 49% of participants who smoked placebo (controls) were also deemed impaired at this same timepoint. Combining a 2 ng/mL THC cutoff in OF with positive findings on FSTs reduced the number of controls classified as impaired to zero, 86 minutes after smoking the placebo. CONCLUSIONS: Requiring a positive toxicology result in addition to the FST observations substantially improved the classification accuracy regarding possible driving under the influence of THC by decreasing the percentage of controls classified as impaired.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".