Urine drug screening for early detection of unwitting use of fentanyl and its analogues among people who inject heroin in Sydney, Australia
Bibliographic record
Abstract
INTRODUCTION AND AIMS: North America has witnessed a dramatic rise in fatal opioid overdoses due to the unwitting consumption of non-pharmaceutical fentanyl and its analogues. While some of the drivers of this crisis-including profitability and access to high-potency opioids through internet sources-also apply in Australia, to our knowledge, there have been no ongoing surveillance studies of local populations. Therefore, this pilot study aimed to detect unintentional fentanyl consumption among people who inject heroin through instant urine screening, and determine the feasibility and acceptability of voluntary urinalysis of clients at the Medically Supervised Injecting Centre, Kings Cross, Sydney. DESIGN AND METHODS: Brief surveys and urine drug screens were conducted with 67 participants in Wave 1 (October 2017) and 51 participants in Wave 2 (March 2018). Urine samples were tested with BTNX Rapid Response™ fentanyl urine strip test at a detection level of 20 ng/mL norfentanyl. These strips also cross-react to numerous fentanyl analogues. RESULTS: There were no cases where positive urine tests suggested unwitting fentanyl use detected in this study. DISCUSSION AND CONCLUSIONS: These negative findings contrast sharply with similar Canadian studies. While no cases of fentanyl-laced heroin use have been detected so far, we have demonstrated that this surveillance design is low-cost, feasible and scalable approach to monitoring the considerable public-health threat of undetected fentanyl and its analogues in Australia. Further validation of cross-reactivity of test strips would strengthen this method.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".