North America laboratory survey data for drug testing in drug-impaired driving and traffic fatality investigations
Bibliographic record
Abstract
In 2004, the National Safety Council's Alcohol, Drugs, and Impairment Division set out to provide guidance for the standardization of laboratory testing practices in driving under the influence of drugs and fatal motor vehicle crash investigations after identifying a lack of consistency in testing practices in this type of casework. A survey about laboratory testing practices, scopes of testing, and cutoffs was created using SurveyMonkey®, an online survey instrument, and sent to laboratories throughout the USA and Canada. Based on the analysis of survey results and discussion, the first set of recommendations was published in 2007 with recommended scope and cutoffs for drug screening and confirmation in blood and urine. Subsequent surveys were sent to laboratories in 2012, 2016, and 2020, followed by updates to the recommendations published in 2013, 2017, and 2021. This publication highlights the 2024 survey results in addition to trends in drug testing practices and drug use positivity. With each survey year, data exhibited a shift of laboratories using newer and more sensitive technology such as liquid chromatography-high-resolution mass spectrometry for screening and confirmation. Overall, data show that laboratories are willing to implement changes to be in compliance with the recommendations; however, challenges with instrument capacity and technology, lack of staffing, training, laboratory space constraints, and time associated with method development and validation hinder compliance with all of the recommendations. While compliance increased, 51% of laboratories reported using the practice of stop-limit testing, an administrative decision to stop testing if a blood alcohol concentration result is at or above a certain concentration, which further hinders the understanding of the drug-impaired driving problem. Delta-9-Tetrahydrocannabinol and/or metabolites remained the most prevalent drug reported by laboratories, followed by stimulants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".