B-325 Retrospective Study Comparing Immunoassay Screen and Mass Spectrometry Confirmation Results in an Opioid Dependant Population
Bibliographic record
Abstract
Abstract Background Retrospective analysis of clinical laboratory data is employed to monitor the performance of instruments, evaluate test utilization and support operational changes. Urine drug testing in the context of opioid use disorder is used to assist in the management of patients. This testing is typically performed by an immunoassay screen followed by mass spectrometry confirmation. Immunoassay screens identify drug class and can cross-react with off-target substances to produce false-positive or false-negative results. Mass spectrometry confirmations provide more sensitive and specific results. The objective of this study was to utilize historical data to determine the concordance between immunoassay and mass spectrometry results from patients in an opioid dependency treatment program. Methods Urine drug screening results spanning a 3-year period (2020–2023) were mined to identify samples that screened positive for amphetamines, benzodiazepines, opiates, fentanyl/carfentanil, oxycodone/oxymorphone, and cocaine. These results were compared to the mass spectrometry confirmation results. The immunoassay results were collected on an Olympus AU480 instrument and the mass spectrometry results were collected using an in-house developed dynamic multiple reaction monitoring method on both Agilent 6470 and 6460 triple quadrupole instruments. Results Of the 15 850 urine drug records reviewed, 28.8% screened positive for amphetamines, 23.5% screened positive for benzodiazepines, 14.2% screened positive for opiates, 23.8% screened positive for fentanyl/carfentanil, 4.2% screened positive for oxycodone/oxymorphone, and 11.2% screened positive for cocaine. Of the amphetamine class samples that screened positive, 95.3% confirmed positive and 4.7% confirmed negative. Of the benzodiazepine class samples that screened positive, 50.8% confirmed positive and 49.2% confirmed negative. Of the opiate class samples that screened positive, 79.8% confirmed positive and 20.2% confirmed negative. Of the fentanyl/carfentanil samples that screened positive, 97.2% confirmed positive and 2.8% confirmed negative. Of the oxycodone/oxymorphone samples that screened positive, 58.5% were confirmed positive and 41.5% confirmed negative. Of the cocaine samples that screened positive, 99.8% confirmed positive and 0.2% confirmed negative. Conclusion Our findings determined there were discrepancies among all classes when comparing immunoassay vs mass spectrometry results. There was good agreement between immunoassay and mass spectrometry results in the detection of fentanyl/carfentanil, cocaine and amphetamines. The differences between methods were most significant in the benzodiazepine, opiate and semi-synthetic opioid classes. This may be due to different cut-off levels between methods, specificity limitations of immunoassays and/or changing drug use patterns resulting in mass spectrometry multiple reaction monitoring false negative results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".