Observational study of visual testing efficacy in detecting cannabis usage
Bibliographic record
Abstract
Drug recognition and examination programs are widely used to detect drug impairment in motor vehicle operators. Visual tests are a key assessment in the detection of cannabis-related impairment. Participants were recruited via social media from the medical cannabis community in Southwestern Ontario, Canada. Twenty-two participants completed the full observational trial design. The majority (n = 13 or 59.1%) were male, with a mean age of 36 years (SD = 9.4; range: 24–59). Participants underwent the following protocol: 1) First round of testing (vital signs, bio sample collection, visual tests, subjective data, neurocognitive testing) (Baseline phase); 2) Consumption of cannabis via inhalation; 3) Second round of testing 30 minutes following consumption (THC phase); 4) Additional rounds of testing at 90, 150, and 210 minutes following consumption (Recovery phase). Visual assessment data and vital signs did not follow typical patterns associated with acute cannabis intoxication. With blood THC levels more than double the Canadian legal limit (5 ng/mL), visual testing results were not diagnostic for cannabis impairment, as participants maintained normal pupil sizes and normal ocular convergence patterns. Visual testing is a key component in standardized examinations used for detecting cannabis-related impairment in Canadian drivers; however, our data indicate that visual testing may not be an effective diagnostic tool for the specific population of medical cannabis users.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".