Do we need urine drug screens in opioid addiction treatment: An observational study on self-report versus urine drug screens
Bibliographic record
Abstract
Background: The substance use crisis continues to progress. Medication for Opioid Use Disorder (MOUD) are prescribed to reduce opioid use and related harms; however, many individuals continue to use substances while on treatment. The objective of this study was to describe the temporal and demographic trends of the agreement between self-reported and urine tested substances. Methods: The current study is a retrospective secondary analysis of three phases of a prospective cohort study (Pilot 2011, Genetics of opioid addiction (GENOA) 2013-2017, and Pharmacogenetics of opioid substitution treatment (POST)) 2018-2022) spanning 2011-2022. We compared the self-reported substance use data for opioids, benzodiazepines, amphetamine/methamphetamine (AMP/MET), and cocaine with urine drug results. We compared the positive predictive value (PPV), false omission rate (FOR), sensitivity, and specificity between (i) different drugs; (ii) by sex, and (iii) age group at enrollment in each phase of the study using self-reported substance use at baseline and retrospective electronic health record data on urine drug screenings collected over the same time period. Results: Overall, the average PPV and FOR for any drug across all phases was 80.7 % and 37.9 %, respectively. Sensitivity and specificity were highest for cocaine and lowest for benzodiazepines. We found no specific trend by sex. Lastly, we found a higher sensitivity for opioids and AMP/MET in those under 25 years of age compared to other age groups. PPV increased over time for benzodiazepines, AMP/MET and cocaine and FOR was higher during the pilot and POST phases than the GENOA phase. Conclusion: Our study highlights the unique challenges associated with ascertaining substance use behaviour for individuals receiving MOUD, indicating many patients will accurately report substance use while others do not. It is therefore important to consider the context of the patient, and the type of the co-substance used to select patient-centred testing as indicated. Therefore, the answer to the question of do we need urine drug screen is yes in some cases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".