Cannabis Impaired Driving: An Evaluation of Current Modes of Detection
Bibliographic record
Abstract
Due to the growing concern with motorists driving under the influence of drugs, the Canadian government has recently implemented legislation to tackle this issue. The new legislation compels drivers to submit to a series of tests, by a police officer, if/when a motorist is suspected of drug impairment. The aim of this paper is to present a review of scientific studies that have evaluated the effectiveness of three methods to detect cannabis use in motorists. These methods include the Drug Evaluation and Classification (DEC) Program, on-site oral fluid screening devices, and on-site urine screening devices. Only studies that included appropriate measures of reliability (i.e., sensitivity, specificity, and accuracy) were included in this review. Given their increasing reliability, on-site oral fluid devices appear to show the most promise for the detection of cannabis use in motorists. Despite the promising results, however, there is still a need to establish standard levels of impairment for cannabis, like the blood alcohol content (BAC) cut-off levels for alcohol, before these devices can be meaningfully utilized and implemented.
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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 | 0.026 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".