Simplifying the Process for Identifying Drug Combinations by Drug Recognition Experts
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to statistically identify the set of drug-related cues from Drug Evaluation and Classification (DEC) evaluations that significantly predict the categories of drugs used by suspected drug-impaired drivers. METHODS: Data from 819 completed Canadian DEC evaluations of combinations of central nervous system (CNS) stimulants with cannabis, CNS stimulants with narcotic analgesics, cannabis with alcohol, and no-drug cases were analyzed using a multinomial logistic regression procedure. RESULTS: Eleven clinical indicators from the DEC evaluations significantly enhanced the prediction of drugs used by suspected drug-impaired drivers, including condition of the eyes, lack of convergence, rebound dilation, reaction to light, mean pulse rate, presence of visible injection sites, performance on the Horizontal Gaze Nystagmus Test, pupil size in darkness, performance on the One-Leg Stand Test, muscle tone, and performance on the Walk-and-Turn Test. CONCLUSIONS: The findings from this study will facilitate the process of identifying the correct categories of drugs ingested by suspected drug-impaired drivers by focusing on critical signs and symptoms of drug influence. This work will have direct and immediate relevance to the training of drug recognition experts (DREs) by providing the foundation for an innovative, statistically based approach to drug classification decisions by DREs. This research will also facilitate the enforcement of drug-impaired driving laws in Canada to help make Canadian roadways safer for all.
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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 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".