Recommendations for toxicological investigation of drug-impaired driving and motor vehicle fatalities—2025 update
Bibliographic record
Abstract
This article describes updates to previously published recommendations for drug testing in drug-impaired driving cases. A survey of drug testing practices in driving under the influence of drug and motor vehicle fatality cases was sent to toxicology laboratories across the USA and Canada. Following the compilation of survey data, a virtual consensus meeting was held where forensic science practitioners and the authors reviewed the survey results and conducted a comprehensive review of the 2021 recommendations using a modified Delphi method. Tier I and Tier II screening and confirmation scope and cutoffs were re-evaluated to update the recommendations. Carisoprodol and meprobamate were moved to the Tier II scope from Tier I; gabapentin was promoted to the Tier I scope from Tier II; screening cutoffs were differentiated for immunoassay versus non-immunoassay (e.g. chromatographic) screening for blood and oral fluid; cross-reactivity screening requirements were removed and clarified with specific cutoff values; several cutoffs for screening and confirmation were increased or removed for blood and oral fluid; urine was removed as a recommended matrix for testing in cases involving suspected drug impairment.
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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 | 0.030 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".