Factors associated with willingness to take extended release naltrexone among injection drug users
Bibliographic record
Abstract
BACKGROUND: Although opioid-agonist therapy with methadone or buprenorphine/naloxone is currently the mainstay of medical treatment for opioid use disorder, these medications often are not well accepted or tolerated by patients. Recently, extended release naltrexone (XR-NTX), an opioid antagonist, has been advanced as an alternative treatment. The willingness of opioid-addicted patients to take XR-NTX has not been well described. METHODS: Opioid-using persons enrolled in a community-recruited cohort in Vancouver, Canada, were asked whether or not they would be willing to take XR-NTX. Logistic regression was used to independently identify factors associated with willingness to take the medication. RESULTS: Among the 657 participants surveyed between June 1, 2013, and November 30, 2013, 342 (52.1%) were willing to take XR-NTX. One factor positively associated with willingness was daily heroin injection (adjusted odds ratio [AOR] = 1.53; 95% confidence interval [CI] = 1.02-2.31), whereas Caucasian ethnicity was negatively associated (AOR = 0.59; 95% CI = 0.43-0.82). Satisfaction with agonist therapy (13.4%) and unwillingness to stop opioids being used for pain (26.9%) were the most common reasons for being unwilling to take XR-NTX. CONCLUSIONS: A high level of willingness to take XR-NTX was observed in this setting. Interestingly, daily injection heroin use was positively associated with willingness, whereas Caucasian participants were less willing to take XR-NTX. Although explanations for unwillingness were described in this study, further research is needed to investigate real-world acceptability of XR-NTX as an additional option for the treatment of opioid use disorder.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".