Development of oral fluid toxicology screening methodologies used to compare commercial and non-commercial driver populations
Bibliographic record
Abstract
Development of a rapid, simple extraction method followed by qualitative screening using liquid chromatography tandem mass spectrometry (LC-MS/MS) for drugs in oral fluid is presented. The decision points were selected to be at, or lower, than those recommended as Tier I compounds by the National Safety Council’s Alcohol, Drugs, and Impairment Division (NSC-ADID) for toxicological investigation of driving under the influence of drugs cases (DUID) and were also at, or lower, than those recommended by Substance Abuse and Mental Health Service Administration (SAMHSA) and the Department of Transportation (DOT) for Federal workplace drug testing programs. In response to the NSC-ADID Tier II recommendations a method for analysis by liquid chromatography–quadrupole time of flight tandem mass spectrometry (LC-QTOF-MS) for drugs in oral fluid collected with the Quantisal™ device has been developed. The decision point cut-off concentrations were at, or below, those recommended toxicological investigation of driving under the influence of drugs cases. The supporting mass spectral-based screening library was adapted from commercially available databases and in-house development included Tier I and II recommended compounds. In 2024, a drug prevalence roadside survey was performed in the Yukon territory of Canada. Volunteers operating motor vehicles on Wednesday through Saturday nights during the months of June through August were asked to donate oral fluid samples and participate in a quick questionnaire of past and present drug use. Samples were collected from 294 non-commercial drivers and 220 commercial drivers. Oral fluid sample collection was chosen as the preferred sample matrix due to the ease of collection for the donor. Drugs in oral fluid are indicative of those compounds circulating 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, 2014). Oral fluid samples were tested using both qualitative screening methods and later confirmed by drug class specific LC-MS/MS analysis. Results highlight the need for more comprehensive DUID testing with drug positivity rates increasing from 16% to 25% in both commercial and non-commercial drivers.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".