Assessing the limit of detection of Fourier‐transform infrared spectroscopy and immunoassay strips for fentanyl in a real‐world setting
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION AND AIMS: Drug checking is a harm reduction intervention increasingly used in the context of the opioid overdose epidemic. The aim of the study was to determine the limit of detection for fentanyl of two point-of-care drug checking technologies. DESIGN AND METHODS: Samples tested at point-of-care using Bruker Fourier transform infrared (FTIR) spectroscopy and BTNX fentanyl immunoassay strips were sent for confirmatory laboratory analysis using quantitative nuclear magnetic resonance (qNMR) spectroscopy. Concentrations by weight were determined and compared to results obtained with point-of-care methods. RESULTS: In total, 283 samples were sent for qNMR analysis; among these, 173 (61.1%) tested positive for fentanyl. As determined by qNMR, fentanyl concentration by weight ranged from 1% to 91%. Among these 173 samples, fentanyl was not detected in 30 (17.3%) samples by FTIR and in 4 (2.3%) samples by test strip. Samples containing fentanyl that went undetected by FTIR had concentrations ≤10%. The four samples containing fentanyl that went undetected by test strip had concentrations ≤5% (i.e. 1%, 3%, 4%, 5%). DISCUSSION AND CONCLUSIONS: Fentanyl immunoassay strips were able to consistently detect the presence of fentanyl in samples at lower concentrations than FTIR spectroscopy. Given that FTIR spectroscopy is able to quantify content, mixture and concentrations on an array of compounds beyond just fentanyl but requires concentrations generally greater than 10%, these findings provide evidence for use of FTIR spectroscopy and immunoassay strips in combination to compensate for the limitations of each technology alone.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it