B-290 A perspective on commonly used drugs in patients receiving treatment for opioid use disorder in Ontario, Canada
Bibliographic record
Abstract
Abstract Background Urine drug screens (UDS) are limited to a few drug classes of interest and are typically done by immunoassay-based (IA) methods. Screening results can then be confirmed by liquid chromatography coupled with tandem mass spectrometry methods (LC-MS/MS) if required. Medication-assisted treatment (MAT) has been used for treatment and monitoring of Opioid Use Disorders (OUD). Medications used in MAT are usually methadone and/or a combination of buprenorphine and naloxone. The aim of this is study was to see what other drugs the participants from the Pharmacogenetics of Opioid Substitution Treatment Response (POST) study in Ontario, Canada, were taking in addition to the prescribed medications. Methods Two hundred POST study participants provided their urine samples to be tested on LC-MS/MS after their urines had been screened by IA. There were 99 drugs tested in this method. The kits were provided by Chromsystems (Grafelfing, Germany) and we followed their procedure for the analysis of these drugs. We investigated the participants’ demographics by age and gender/sex, the most commonly used drugs, their most common combinations, and the number of participants who were taking = 2 drugs, confirmed by LC-MS/MS. We also checked discordances between IA and LC-MS/MS for MATs, and amphetamine and methamphetamine. All the analyses and pertaining graphs were done in Microsoft Excel (Microsoft Corporation). Results The average age for all participants was 39.5 years. The participants were divided into 5 age groups (20-29.9, 30-30.9, 40-49.9, 50-50.9, = 60) and two sexes (males and cis/trans-females). Majority (n=161, 80.5%) of the participants were of the European descent, and 43.0 % were females. The three most commonly abused drugs were selected for a more detailed demographics analysis: THC-COOH (n=96, 48%), amphetamine and methamphetamine (n=47, 23.5%, for both). THC-COOH was present in 11.5% participants aged 20-29.9, 16.5% aged 30-39.9, 10% aged 40-49.9, 8% aged 50-59.9 and 2% = 60 years of age. THC-COOH was present in 31.5% males and 16% females (0.5% trans-females). Both amphetamine and methamphetamine were present in 23.5% of the participants (n=47), and both were present in 15% males and 8.5% females, as expected. There was only a slight difference between age groups. The percentage of the most common drug combination was as follows: amphetamine and methamphetamine (42%); amphetamine, methamphetamine combined with THC-COOH (19%); and amphetamine, methamphetamine combined with norfentanyl (18%). The greatest number of participants who took = 2 drugs confirmed by LC-MS/MS (n=40, 20%) had 3 drugs in urine. Surprisingly, 26.5% of participants who were compliant with their MATs, confirmed by LC-MS/MS, were negative on IA screens. Thirty-one participants (15.5%) were falsely positive for either amphetamine or methamphetamine on IA, after being confirmed negative on LC-MS/MS. Conclusion This work has shed important light into what populations across Ontario, who are receiving MAT for OUD, concurrently use in addition to their prescribed treatment. It confirmed the importance of LC-MS/MS for confirmation of the compliance with MAT, as well as confirmation of commonly taken illicit drugs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".