Correlation between oral fluid and blood THC concentration: A systematic review and discussion of policy implications
Bibliographic record
Abstract
Cannabis is the second most commonly used impairing substance by drivers, after alcohol. As more countries legalize cannabis, there is concern that cannabis-impaired driving will increase. In many countries, police use roadside devices to test for oral fluid THC (the primary psychotropic component in cannabis) to identify drivers who used cannabis; including in countries with non-zero per se limits for THC in blood. This practice is questioned as previous research demonstrates a poor correlation between oral fluid and blood THC concentrations at the individual level. We conducted a meta-analysis to identify all research that compared oral fluid with blood THC levels. We obtained individual-level data from study authors and analyzed pooled individual-level data to calculate sensitivity and specificity of oral fluid THC (at various cut-off values) to detect blood THC above different concentration limits. Finally, we explored practical implications of using oral fluid THC in an enforcement context. Our review found THC concentrations measured in over 18,000 paired samples of oral fluid and blood. We found a good correlation between the presence of THC in oral fluid and presence of THC in blood (sensitivity = 71.2%, specificity = 97.7%). However oral fluid THC, at commonly used cut-off values, is less sensitive and less specific when used as a biomarker to detect people with blood THC concentrations above commonly used per se limits (such as 5 ng/mL). As such, there will be a large number of "false positive" tests if oral fluid THC testing were used as a biomarker for "illegal" THC concentrations in randomly selected drivers. We argue that the adverse implications of false positive oral fluid THC tests in this context outweigh the possible road safety benefits and we recommend against oral fluid THC screening in randomly selected drivers in countries with non-zero per se limits for blood THC. In contrast, oral fluid THC tests appear to be useful for investigating "high-risk" drivers who come to police attention because of evidence of impairment.
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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 | 0.025 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".