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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".