The Utility of THC Cutoff Levels in Blood and Saliva for Detection of Impaired Driving
Bibliographic record
Abstract
Background: Δ9-Tetrahydrocannabinol (THC) is the psychoactive component in cannabis and a relationship of THC to driving impairment is expected. Despite this, there are discrepant findings with respect to the relationship of blood THC to driving. This study investigated the relationship of blood, urine, and saliva THC/THC-COOH levels to “weaving,” as measured by a driving simulator. Methods: Participants smoked cannabis alone or with alcohol. THC/THC-COOH levels in blood, urine, and saliva were correlated with standard deviation of lateral position (SDLP), measuring “weaving.” In addition, SDLP after cannabis and/or alcohol were compared with SDLP after placebo when THC/THC-COOH levels were above or below specified thresholds in blood (5 ng/mL), urine (50 ng/mL), or saliva (25 ng/mL). Results: A clear linear relationship between blood THC concentration and SDLP was not observed based on calculation of Spearman coefficients. When compared with placebo, SDLP was significantly increased after cannabis and cannabis combined with alcohol when THC in the blood was above the legal limit. SDLP was increased in drug conditions when saliva cutoffs were above the legal limit. Conclusions: The findings of this study suggest that specified thresholds for THC in blood and saliva may be able to detect driving impairment, but future studies are needed. ClinicalTrials.gov ID: NCT03106363.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".