An assessment of the limits of detection, sensitivity and specificity of three devices for public health-based drug checking of fentanyl in street-acquired samples
Bibliographic record
Abstract
BACKGROUND: Fentanyl has caused rapid increases in US and Canadian overdose deaths, yet its presence in illicit drugs is often unknown to consumers. This study examined the validity in identifying the presence of fentanyl of three portable devices that could be used in providing drug checking services and drug supply surveillance: fentanyl test strips, a hand-held Raman Spectrometer, and a desktop Fourier-Transform Infrared Spectrometer. METHODS: In Fall 2017, we first undertook an assessment of the limits of detection for fentanyl, then tested the three devices' sensitivity and specificity in distinguishing fentanyl in street-acquired drug samples. Utilizing test replicates of standard fentanyl reference material over a range of increasingly lower concentrations, we determined the lowest concentration reliably detected. To establish the sensitivity and specificity for fentanyl, 210 samples (106 fentanyl-positive, 104 fentanyl-negative) previously submitted by law enforcement entities to forensic laboratories in Baltimore, Maryland, and Providence, Rhode Island, were tested using the devices. All sample testing followed parallel and standardized protocols in the two labs. RESULTS: The lowest limit of detection (0.100 mcg/mL), false negative (3.7%), and false positive rate (9.6%) was found for fentanyl test strips, which also correctly detected two fentanyl analogs (acetyl fentanyl and furanyl fentanyl) alone or in the presence of another drug, in both powder and pill forms. While less sensitive and specific for fentanyl, the other devices conveyed additional relevant information including the percentage of fentanyl and presence of cutting agents and other drugs. CONCLUSION: Devices for fentanyl drug checking are available and valid. Drug checking services and drug supply surveillance should be considered and researched as part of public health responses to the opioid overdose crisis.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".