Drug prevalence in Canadian driving population
Bibliographic record
Abstract
In 2024 a drug prevalence roadside survey was performed in the Yukon territory of Canada. Volunteer drivers on Wednesday through Saturday nights June through August 2024 were asked to donate oral fluid samples and participate in a questionnaire of drug use. Samples were collected from 294 noncommercial and 220 commercial drivers. Oral fluid sample collection was chosen due to the ease of collection for the donor. Drugs in oral fluid are indicative of those compounds in the blood at the time of collection. Drugs are deposited in oral fluid by diffusion from blood or coating the oral mucosa. Studies have shown similar drug class results when oral fluid and blood are compared (Kelley-Baker et al. 2014). The objective of this paper is to perform additional screening by liquid chromatography time of flight mass spectrometry (LC-QTOF-MS) on all samples for the Tier I and II compounds as suggested by NSC-ADID recommendations. Oral fluid samples were collected at the roadside using the Quantisal<sup>™</sup> collection device and shipped overnight to 9 Delta Analytical, LLC where they were screened for delta 9-tetrahydrocannabinal, amphetamines, cocaine, benzodiazepines, and opiates including fentanyl. Initial screening was performed using a liquid chromatograph tandem mass spectrometer (LC-MS/MS). Screening parameters were fully validated and published in a peer reviewed journal (Coulter et al. 2022). All samples screening positive were confirmed by LC-MS/MS using a second sample aliquot. Additional testing resulted in an increased positivity rate for both driving cohorts. Positivity rates increased to 25% for both groups with polydrug use seen in 7.5% and 4.5% for noncommercial and commercial drivers respectively. Statistical analysis showed THC concentrations were different between the two driving groups. The mean concentration for THC in noncommercial drivers was 29 ng/mL compared to 8 ng/mL for commercial drivers, when high concentration outliers were removed. Additional discoveries of over the counter and antidepressant medication were made using Tier I and II recommendations. The NSC-ADID Tier I and II recommendations should be followed when conducting drug prevalence surveys of drivers. LC-QTOF-MS is the recommended tool to conduct drug screening for oral fluid samples.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.294 | 0.001 |
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".