The future of precision medicine in opioid use disorder: inclusion of patient-important outcomes in clinical trials
Bibliographic record
Abstract
Opioid use has reached an epidemic proportion in Canada and the United States that is mostly attributed to excess availability of prescribed opioids for pain. This excess in opioid use led to an increase in the prevalence of opioid use disorder (OUD) requiring treatment. The most common treatment recommendations include medication-assisted treatment (MAT) combined with psychosocial interventions. Clinical trials investigating the effectiveness of MAT, however, have a limited focus on effectiveness measures that overlook patient-important outcomes. Despite MAT, patients with OUD continue to suffer negative consequences of opioid use. Patient goals and personalized medicine are overlooked in clinical trials and guidelines, thus missing an opportunity to improve prognosis of OUD by considering precision medicine in addiction trials. In this mixed-methods study, patients with OUD receiving MAT (n=2,031, mean age 39.1 years [SD 10.7], 44% female) were interviewed to identify patient goals for MAT. The most frequently reported patient-important outcomes were to stop treatment (39%) and to avoid all drugs (25%). These results are inconsistent with treatment recommendations and trial outcome measures. We discuss theses inconsistencies and make recommendations to incorporate these outcomes to achieve patient-centered and personalized treatment strategies.
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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 | 0.549 | 0.686 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".