Impact of the Opioid/Fentanyl Epidemic on the Toxicology Laboratory’s Workload: The CMCVAMC Experience
Bibliographic record
Abstract
Abstract Introduction Opiates have long been used by both the population at large and the veteran population as a drug of abuse. However, recently, fentanyl—a synthetic opioid—has risen in prominence in this opioid drug abuse epidemic as a drug used by suppliers to “cut” heroin, to masquerade for another opiate, or for direct usage. As this is a recent phenomenon, the new increasing need to test for fentanyl for clinical reasons has a major impact on the toxicology laboratory’s workload. Method Quality assurance/improvement data were obtained to determine the number of fentanyl tests by gas chromatography/mass spectroscopy (GC/MS) performed by the toxicology laboratory since quarter 1 of 2011 (October-December 2010) to quarter 1 of 2018 (October-December 2017). The numbers of tests required for clinical care in each quarter were tabulated and compared in a graph. Quarters for each year begin and end in October. Results The total number of GC/MS tests for fentanyl needed for clinical care has been drastically increasing recently. From 2011 to 2015, the yearly number of tests clinically needed has ranged from 83 to 92. In 2016, the total number of clinically needed tests for fentanyl spiked to 167 and by fiscal year 2017 included 1,108 fentanyl GC/MS tests. The last examined quarter (quarter 1 of fiscal year 2018) included 527 tests, which is more than the highest number from 2017 (377 in quarter 4 of 2017). Conclusion The increasing use of fentanyl in the opioid epidemic appears to have played a role in significantly increasing the clinical need to test for fentanyl by GC/MS, increasing the volume by over 10 to 15 times. The role of fentanyl in the opioid epidemic remains a significant public health concern.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".