Evaluation of the Usefulness of An Oxycodone Immunoassay in Combination with a Traditional Opiate Immunoassay for the Screening of Opiates in Urine
Bibliographic record
Abstract
Oxycodone is a semisynthetic opioid analgesic largely prescribed for post-operative and chronic pain management. The introduction of a slow release formulation of oxycodone has led to its frequent abuse and to an increase in emergency cases related to oxycodone overdose. Until recently, oxycodone testing has been confined to gas chromatography-mass spectrometry (GC-MS) analysis because the widely used automated opiate immunoassays poorly react to this compound. We investigated the utility of a new oxycodone immunoassay as a screening procedure to eliminate inappropriate GC-MS testing of negative urine specimens. We analyzed 96 urine specimens using GC-MS and two immunoassays, CEDIA((R)) opiates and DRI((R)) oxycodone assays from Microgenics, on a Hitachi 917 analyzer. The GC-MS allowed us to detect codeine, hydrocodone, hydromorphone, morphine, oxycodone, and oxymorphone following enzymatic hydrolysis and derivation by acetylation. The combination of the two immunoassays gave the best performance (98% sensitivity and specificity) when considering a positive result from GC-MS for any of the opiates. Considering positive GC-MS results for oxycodone or oxymorphone only, the oxycodone immunoassay resulted in two false-positives and one false-negative (50 ng/mL cutoff). Using these immunoassays for screening before GC-MS analysis provides a reduced opiate GC-MS workload without compromising quality.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".