Primary care management of opioid use disorders: Abstinence, methadone, or buprenorphine-naloxone?
Bibliographic record
Abstract
OBJECTIVE: To advise physicians on which treatment options to recommend for specific patient populations: abstinence-based treatment, buprenorphine-naloxone maintenance, or methadone maintenance. SOURCES OF INFORMATION: PubMed was searched and literature was reviewed on the effectiveness, safety, and side effect profiles of abstinence-based treatment, buprenorphine-naloxone treatment, and methadone treatment. Both observational and interventional studies were included. MAIN MESSAGE: Both methadone and buprenorphine-naloxone are substantially more effective than abstinence-based treatment. Methadone has higher treatment retention rates than buprenorphine-naloxone does, while buprenorphine-naloxone has a lower risk of overdose. For all patient groups, physicians should recommend methadone or buprenorphine-naloxone treatment over abstinence-based treatment (level I evidence). Methadone is preferred over buprenorphine-naloxone for patients at higher risk of treatment dropout, such as injection opioid users (level I evidence). Youth and pregnant women who inject opioids should also receive methadone first (level III evidence). If buprenorphine-naloxone is prescribed first, the patient should be promptly switched to methadone if withdrawal symptoms, cravings, or opioid use persist despite an optimal buprenorphine-naloxone dose (level II evidence). Buprenorphine-naloxone is recommended for socially stable prescription oral opioid users, particularly if their work or family commitments make it difficult for them to attend the pharmacy daily, if they have a medical or psychiatric condition requiring regular primary care (level IV evidence), or if their jobs require higher levels of cognitive functioning or psychomotor performance (level III evidence). Buprenorphine-naloxone is also recommended for patients at high risk of methadone toxicity, such as the elderly, those taking high doses of benzodiazepines or other sedating drugs, heavy drinkers, those with a lower level of opioid tolerance, and those at high risk of prolonged QT interval (level III evidence). CONCLUSION: Individual patient characteristics and preferences should be taken into consideration when choosing a first-line opioid agonist treatment. For patients at high risk of dropout (such as adolescents and socially unstable patients), treatment retention should take precedence over other clinical considerations. For patients with high risk of toxicity (such as patients with heavy alcohol or benzodiazepine use), safety would likely be the first consideration. However, the most important factor to consider is that opioid agonist treatment is far more effective than abstinence-based treatment.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".