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 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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".