An assessment of oral fluid drug screening devices
Bibliographic record
Abstract
This project was to examine point-of-contact (POC) oral fluid drug screening devices to determine the suitability of such devices for potential use in the enforcement of drug-impaired driving in Canada. Oral fluid samples were collected from a group of individuals who admitted to having recently ingested drugs as well as a number of individuals who had not been using drugs. These samples were tested on one of three oral fluid screening devices to determine the presence of cannabis, cocaine, amphetamine, methamphetamine, opioids, and benzodiazepines. Each participant also provided a second oral fluid sample that was sent to a reference laboratory for independent analysis. Comparison of the results from the oral fluid screening device and those from the laboratory analysis provided estimates of sensitivity and specificity for each of the six drugs/drug categories. Sensitivity exceeded 0.80 for cannabis, cocaine, methamphetamine, and opioids. False positive rates for these drugs/drug categories were all between 3% and 7%. Specificity exceeded 0.90 for all drugs/drug categories. These findings indicate that oral fluid screening could prove to be a valuable tool in the detection of driver drug use in Canada.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".