ArticleAddiction (Abingdon, England)2024
Empirically contrasting urine drug screening-based opioid use disorder treatment outcome definitions.
Article in Addiction (Abingdon, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Applying machine learning in predicting medication treatment outcomes for opioid use disorder.Journal of substance use and addiction treatment · 2026Article
- Engaging an advisory board in discussions about the ethical relevance of algorithmic bias and fairness.NPJ digital medicine · 2025Review
- Treating Opioid Use Disorder and Opioid Withdrawal in the Context of Fentanyl.Annual review of clinical psychology · 2025Review
- Data cleaning and harmonization of clinical trial data: Medication-assisted treatment for opioid use disorder.PloS one · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
BACKGROUND AND
aimsA lack of consensus on the optimal outcome measures to assess opioid use disorder (OUD) treatment efficacy and their precise definition and computation has hampered the pooling of research data for evidence synthesis and meta-analyses. This study aimed to empirically contrast multiple clinical trial definitions of treatment success by applying them to the same dataset.
methodsData analysis used a suite of functions, developed as a software package for the R language, to operationalize 61 treatment outcome definitions based on urine drug screening (UDS) results. Outcome definitions were derived from clinical trials that are among the most influential in the OUD treatment field. Outcome functions were applied to a harmonized dataset from three large-scale National Drug Abuse Treatment Clinical Trials Network (CTN) studies, which tested various medication for OUD (MOUD) options (n = 2492). Hierarchical clustering was employed to empirically contrast outcome definitions.
resultsThe optimal number of clusters identified was three. Cluster 1, comprising eight definitions focused on detecting opioid-positive UDS, did not include missing UDS in outcome calculations, potentially resulting in inflated rates of treatment success. Cluster 2, with the highest variability, included 10 definitions characterized by strict criteria for treatment success, relying heavily on UDS results from either a brief period or a single study visit. The 43 definitions in Cluster 3 represented a diverse range of outcomes, conceptualized as measuring abstinence, use reduction and relapse. These definitions potentially offer more balanced measures of treatment success or failure, as they avoid the extreme methodologies characteristic of Clusters 1 and 2.
conclusionsClinical trials using urine drug screening (UDS) for objective substance use assessment in outcome definitions should consider (1) incorporating missing UDS data in outcome computation and (2) avoiding over-reliance on UDS data confined to a short time frame or the occurrence of a single positive urine test following a period of abstinence.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.