Buprenorphine-naloxone microdosing
Bibliographic record
Abstract
Abstract Objective To raise awareness of alternative techniques that can facilitate buprenorphine-naloxone treatment for opioid use disorder. Sources of information PubMed was searched for articles using the terms buprenorphine, buprenorphine/naloxone, micro-dosing, opioid agonist therapy, and induction. Other relevant guidelines, presentations, and resources were consulted. Main message Buprenorphine-naloxone is the first-line option for opioid agonist therapy owing to its superior safety profile compared with methadone. The uptake of this potentially life-saving drug has been limited by unfamiliarity and prescribing restrictions, but perhaps the biggest barrier is the prerequisite that patients be in moderate to severe withdrawal before initiation. An induction option that does not require withdrawal or immediate cessation of current opioid use, termed microdosing, is an appealing choice for patients and a practical approach that can be used by a broader array of practitioners, ultimately increasing access to buprenorphine-naloxone. Family physicians play an important role in the current opioid crisis by helping patients transition to opioid agonist therapy. Conclusion Microdosing is a safe and easy-to-implement regimen that can be used in a variety of practice settings with the help of community pharmacists. This article provides an overview of microdosing and serves as a guide to starting and maintaining treatment.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.008 |
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".