Development of a neural network model to predict the presence of fentanyl in community drug samples
Bibliographic record
Abstract
INTRODUCTION: Increasingly, Fourier-transform infrared (FTIR) spectroscopy is being used as a harm reduction tool to provide people who use drugs real-time information about the contents of their substances. However, FTIR spectroscopy has been shown to have a high detection limit for fentanyl and interpretation of results by a technician can be subjective. This poses concern, given that some synthetic opioids can produce serious toxicity at sub-detectable levels. The objective of this study was to develop a neural network model to identify fentanyl and related analogues more accurately in drug samples compared to traditional analysis by technicians. METHODS: Data were drawn from samples analyzed point-of-care using combination FTIR spectroscopy and fentanyl immunoassay strips in British Columbia between August 2018 and January 2021. We developed neural network models to predict the presence of fentanyl based on FTIR data. The final model was validated against the results from immunoassay strips. Prediction performance was assessed using F1 score, accuracy, and area under the receiver-operating characteristic curve (AUROC), and was compared to results obtained from analysis by technicians. RESULTS: A total of 12,684 samples were included. The neural network model outperformed results from those analyzed by technicians, with an F1 score of 96.4% and an accuracy of 96.4%, compared to 78.4% and 82.4% with a technician, respectively. The AUROC of the model was 99.0%. Fentanyl positive samples correctly detected by the model but not by the technician were typically those with low fentanyl concentrations (median: 2.3% quantity by weight; quartile 1-3: 0.0%-4.6%). DISCUSSION: Neural network models can accurately predict the presence of fentanyl and related analogues using FTIR data, including samples with low fentanyl concentrations. Integrating this tool within drug checking services utilizing FTIR spectroscopy has the potential to improve decision making to reduce the risk of overdose and other negative health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".