Recommendations for Toxicological Investigation of Drug-Impaired Driving and Motor Vehicle Fatalities—2021 Update
Bibliographic record
Abstract
This report describes updates to the National Safety Council's Alcohol, Drugs and Impairment Division's recommendations for drug testing in driving under the influence of drug (DUID) cases and motor vehicle fatalities. The updates are based on a survey of drug testing practices in laboratories in the USA and Canada, a comprehensive review of the prior recommendations and data and research on drugs most frequently detected in DUID cases. A consensus meeting was held with representative forensic science practitioners and the authors of this report to update recommendations. No changes were made to the Tier I scope; however, there were changes to cutoffs of some analytes for blood, urine and oral fluid. Due to increased prevalence in DUID cases, trazodone and difluoroethane were added to the Tier II scope. For clarification, Tier I cutoffs reflect free concentrations, and hydrolysis is recommended but not required. The consensus panel concluded that urine is an inferior matrix to blood and oral fluid as it may represent historical use or exposure unrelated to observed impairment; therefore, future iterations of these recommendations will not include urine as a recommended matrix. Laboratories currently testing urine should work with traffic safety partners to encourage the use of blood and oral fluid as more appropriate specimens and adjust their capabilities to provide that testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.021 |
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".