Genetic influence on methadone treatment outcomes in patients undergoing methadone maintenance treatment for opioid addiction: a pilot study
Bibliographic record
Abstract
INTRODUCTION: Treatment of opioid addiction with methadone is effective; however, it is known to produce interindividual variability. This may be influenced in part by genetic variants, which can increase the initial risk of developing opioid addiction as well as explain differences in response to treatment. This pilot study aimed to assess the feasibility of conducting a full-scale genetic analysis to identify genes that predict methadone treatment outcomes in this population. METHODS: This was a cross-sectional observational study of patients admitted to a methadone maintenance treatment program for opioid addiction. We obtained demographic and clinical characteristics in addition to blood and urine samples, for the assessment of treatment outcomes. RESULTS: The recruitment process yielded 252 patients, representing a 20% recruitment rate. We conducted genetic testing based on a 99.6% rate of provision of DNA samples. The average retention in treatment was 3.4 years, and >50% of the participants reported psychiatric and medical comorbidities. BDNF rs6265 and DRD2 rs1799978 were the common single nucleotide polymorphisms (SNPs) selected for the feasibility study. DISCUSSION: This study met our predetermined feasibility criteria; recruitment, response rates, and genetic testing were feasible; treatment duration was sufficient for follow up; and the prevalence of comorbid conditions indicated the need for reliable psychiatric and chronic pain measures. The study strengths included effective collaboration with clinics and the generalizability of sample population. Key learning points show the need for assessment of treatment outcomes on multiple domains, implementation of follow up, and the development of standardized training for the study clinical staff.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".