Roadside screening tests for cannabis use: A systematic review
Bibliographic record
Abstract
As more countries legalize recreational cannabis, roadside screening programs are imperative to detect and deter driving under the influence of cannabis. This systematic review evaluated roadside screening tests for cannabis use. We searched six databases (inception-March 2020) and grey literature sources for primary studies evaluating test characteristics of roadside screening tests for cannabis use compared to laboratory tests for cannabinoids in blood or oral fluid. The synthesis was focused on sensitivity and specificity of delta-9-tetrahydrocannabinol (THC) detection. 101 studies were included. Oral fluid tests were higher in specificity and lower in sensitivity compared to urine tests when evaluated against blood laboratory tests. Oral fluid tests were higher in sensitivity and similar in specificity compared to observational tests when evaluated against blood and oral fluid laboratory tests. Sensitivity was variable among oral fluid tests; two instrumented immunoassays (Draeger DrugTest 5000 [5 ng/mL THC cut-off] and Alere DDS 2 Mobile Test System) appeared to perform best, but definitive conclusions could not be drawn due to imprecise estimates. Specificities were similar. Overall, oral fluid tests showed the most promise for use in roadside screening for blood THC levels over legal limits; their continued development and testing are warranted. Urine tests are generally inadvisable, and observational tests require sensitivity improvements.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".