The North American opioid epidemic: opportunities and challenges for clinical laboratories
Bibliographic record
Abstract
Since 1999, the opioid epidemic in North America has resulted in over 1 million deaths, and it continues to escalate despite numerous efforts in various arenas to combat the upward trend. Clinical laboratories provide drug testing to support practices such as emergency medicine, substance use disorder treatment, and pain management; increasingly, these laboratories are collaborating in novel partnerships including drug-checking services (DCS) and multidisciplinary treatment teams. This review examines drug testing related to management of licit and illicit opioid use, new technologies and test strategies employed by clinical laboratories, barriers hindering laboratory response to the opioid epidemic, and areas for improvement and standardization within drug testing. Literature search terms included combinations of "opioid," "opiate," "fentanyl," "laboratory," "epidemic," "crisis," "mass spectrometry," "immunoassay," "drug screen," "drug test," "guidelines," plus review of PubMed "similar articles" and references within publications. While immunoassay (IA) and point-of-care (POC) test options for synthetic opioids are increasingly available, mass spectrometry (MS) platforms offer the greatest flexibility and sensitivity for detecting novel, potent opioids. Previously reserved as a second-tier application in most drug test algorithms, MS assays are gaining a larger role in initial screening for specific patients and DCS. However, there are substantial differences among laboratories in terms of updating test menus, algorithms, and technologies to meet changing clinical needs. While some clinical laboratories lack the resources and expertise to implement MS, many are also slow to adopt available IA and POC tests for newer opioids such as fentanyl. MS-based testing also presents challenges, including gaps in available guidance for assay validation and ongoing performance assessment that contribute to a dramatic lack of standardization among laboratories. We identify opportunities for improvement in laboratory operations, reporting, and interpretation of drug test results, including laboratorian and provider education and laboratory-focused guidelines. We also highlight the need for collaboration with providers, assay and instrument manufacturers, and national organizations to increase the effectiveness of clinical laboratory and provider efforts in preventing morbidity and mortality associated with opioid use and misuse.
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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 | 0.029 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".