Managing opioid use disorder in primary care: PEER simplified guideline.
Bibliographic record
Abstract
OBJECTIVE: To use the best available evidence and principles of shared, informed decision making to develop a clinical practice guideline for a simplified approach to managing opioid use disorder (OUD) in primary care. METHODS: Eleven health care and allied health professionals representing various practice settings, professions, and locations created a list of key questions relevant to the management of OUD in primary care. These questions related to the treatment setting, diagnosis, treatment, and management of comorbidities in OUD. The questions were researched by a team with expertise in evidence evaluation using a series of systematic reviews of randomized controlled trials. The Guideline Committee used the systematic reviews to create recommendations. RECOMMENDATIONS: Recommendations outline the role of primary care in treating patients with OUD, as well as pharmacologic and psychotherapy treatments and various prescribing practices (eg, urine drug testing and contracts). Specific recommendations could not be made for management of comorbidities in patients with OUD owing to limited evidence. CONCLUSION: The recommendations will help simplify the complex management of patients with OUD in primary care. They will aid clinicians and patients in making informed decisions regarding their care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".