COGNITIVE FUNCTION IN PATIENTS WITH OPIOID USE DISORDER TREATED WITH OPIUM TINCTURE AND METHADONE
Bibliographic record
Abstract
Objectives:We aimed to compare cognitive function in patients with opioid use disorder (OUD) treated with opium tincture (OT) and methadone.Background:There has been an increasing interest in using OT for treating OUD in certain parts of the world such as Iran. OT is an attractive option in such countries because of its widespread availability, cultural acceptability, and low cost. Despite a growing body of research suggesting an association between methadone maintenance treatment (MMT) and cognitive impairment, many questions still remain unanswered - particularly if the impairment is a direct result of MMT and what the underlying mechanisms are. Furthermore, there are currently no studies that have examined OTu2019s effects on cognitive function. This study addresses these gaps by comparing cognitive function in patients treated with OT or methadone over the course of a clinical trial.Materials and Methods:A multi-center, double-blind, non-inferiority randomized controlled trial comparing OT and methadone was completed. A sample of 204 participants with OUD was randomized to OT or methadone with an allocation ratio of 1:1 using a patient-centered flexible dosing strategy. Participants were followed for 12 weeks. A key secondary outcome was cognition, measured by the Montreal Cognitive Assessment (MoCA). Participants completed the MoCA four times u2013 at baseline, week 5, week 9, and week 12. Results and Conclusions:The data is currently being analyzed. Evaluating cognitive function in patients with OUD receiving medication-assisted treatment (MAT) is crucial as it affects patientsu2019 everyday functioning and rehabilitation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".