The impact of methamphetamine use on medications for opioid use disorder (MOUD) treatment retention: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: An emerging public health threat of methamphetamine/opioid co-use is occurring in North America, including increases in overdoses related to concomitant methamphetamine/opioid use. This presents a potential risk to established treatments for opioid use disorder (i.e., medications for opioid use disorder [MOUD]). To date, few studies have examined the impact of methamphetamine use on MOUD-related outcomes, and no studies have synthesized data on MOUD retention. METHODS: A scoping review was undertaken to examine the impact of methamphetamine use on MOUD retention. All original published research articles were searched in Embase, MEDLINE, PsychINFO, CINAHL, Scopus, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and Cochrane Protocols, and Google scholar databases. Data were extracted into a standardized data extraction chart. Findings were presented narratively. RESULTS: All eight included studies demonstrated an increased likelihood of treatment discontinuation or dropout among patients enrolled in MOUD who used methamphetamine. The frequency of methamphetamine use was also associated with MOUD dropout, in that those who used methamphetamine more often were more likely to discontinue MOUD. The definitions and measurements of MOUD retention varied considerably, as did the magnitude of effect size. CONCLUSIONS: Results indicate that methamphetamine use has an undesirable impact on MOUD retention and results in an increased risk of treatment discontinuation or dropout. Strategies to identify concurrent methamphetamine use among individuals engaging in MOUD and educate them on the increased risk for dropout should be undertaken. Further research is needed to understand how MOUD retention among patients with concomitant opioid and methamphetamine use can be improved.
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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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it