An urgent need for community lot testing of lateral flow fentanyl test strips marketed for harm reduction in Northern America
Bibliographic record
Abstract
BACKGROUND: Fentanyl test strips (FTS) are lateral flow immunoassay strips designed for detection of ng/mL levels of fentanyl in urine. In 2021, the US Centers for Disease Control and the Substance Abuse and Mental Health Administration stated that federal funds could be used for procurement of FTS for harm reduction strategies approved by the government such as drug checking. The market for FTS has expanded rapidly in the US and Canada. However, there is no regulatory oversight by either government to ensure proper function of FTS that are being marketed for drug checking. MAIN BODY: Many brands of FTS have rapidly entered the harm reduction market, creating concerns about the reproducibility and accuracy of their performance from brand to brand and lot to lot. Some examples are provided in this Comment. Similar problems with product quality were observed in the mid 2000's when lateral flow immunoassays for malaria were funded in many countries and again in 2020, when COVID-19 tests were in huge demand. The combination of high demand and low levels of regulation and enforcement led some manufacturers to join the goldrush without adequate field testing or quality assurance. We argue that the harm reduction community urgently needs to set a lot checking program in place. A set of simple protocols for conducting the tests and communicating the results have been developed, and are described in the following Perspectives paper in this issue. CONCLUSION: In the absence of governmental regulation and enforcement, the harm reduction community should implement a FTS lot checking program. Based on previous experience with the malaria diagnostic lot checking program, this inexpensive effort could identify products that are not suitable for harm reduction applications and provide valuable feedback to manufacturers. Dissemination of the results will help harm reduction organizations to ensure that FTS they use for drug checking are fit for the purpose.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".