Detection of benzodiazepines in the unregulated drug supply using point of care and confirmatory drug checking technologies: A validation study
Bibliographic record
Abstract
OBJECTIVE: Benzodiazepine adulteration in British Columbia's (BC) unregulated opioid supply has risen. Given the health risks associated with co-ingestion of opioids and benzodiazepines, accurate detection of benzodiazepines using point-of-care drug checking technologies is critical. This study aimed to validate the use of Fourier-transform infrared spectroscopy (FTIR) and benzodiazepine immunoassay strips compared to gold standard laboratory technologies. METHODS: From October 2018 to November 2023, drug samples submitted to harm reduction sites in BC were analyzed using FTIR and benzodiazepine immunoassay strips. A subset of these samples was sent for confirmatory analysis using quantitative nuclear magnetic resonance spectroscopy, gas chromatography-mass spectrometry, and/or liquid chromatography-mass spectrometry. We calculated measures of diagnostic accuracy (e.g., sensitivity, specificity) for the point-of-care technologies. RESULTS: Of 1922 samples with point-of-care and confirmatory results, 390 (20 %) tested positive for a benzodiazepine. FTIR sensitivity was 26 % (95 % confidence interval [CI]:21-30 %) and specificity was 99 % (95 % CI:99-100 %). Immunoassay strip sensitivity was 67 % (95 % CI:62-72 %) and specificity was 82 % (95 % CI:79-85 %), respectively. When FTIR and immunoassay strip results were combined, sensitivity was 75 % (95 % CI:70-79 %) and specificity was 82 % (95 % CI: 79-84 %). When etizolam was excluded, the sensitivity and specificity of immunoassay strips were 98 % (95 % CI:94-99 %) and 83 % (95 % CI:81-86 %), respectively. CONCLUSIONS: FTIR did not consistently detect the presence of benzodiazepines and related compounds at point-of-care. However, sensitivity improved when FTIR was combined with immunoassay strips, underscoring the importance of using both technologies in tandem. As etizolam is not a true benzodiazepine, it poses considerable challenges using existing point-of-care drug checking technologies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".