Treatment of Chronic Pain With Various Buprenorphine Formulations: A Systematic Review of Clinical Studies
Bibliographic record
Abstract
Clinical studies demonstrate that buprenorphine is a pharmacologic agent that can be used for the treatment of various types of painful conditions. This study investigated the efficacy of 5 different types of buprenorphine formulations in the chronic pain population. The literature was reviewed on PubMed/MEDLINE, EMBASE, Cochrane Database, clinicaltrials.gov, and PROSPERO that dated from inception until June 30, 2017. Using the population, intervention, comparator, and outcomes method, 25 randomized controlled trials were reviewed involving 5 buprenorphine formulations in patients with chronic pain: intravenous buprenorphine, sublingual buprenorphine, sublingual buprenorphine/naloxone, buccal buprenorphine, and transdermal buprenorphine, with comparators consisting of opioid analgesics or placebo. Of the 25 studies reviewed, a total of 14 studies demonstrated clinically significant benefit with buprenorphine in the management of chronic pain: 1 study out of 6 sublingual and intravenous buprenorphine, the only sublingual buprenorphine/naloxone study, 2 out of 3 studies of buccal buprenorphine, and 10 out of 15 studies for transdermal buprenorphine showed significant reduction in pain against a comparator. No serious adverse effects were reported in any of the studies. We conclude that a transdermal buprenorphine formulation is an effective analgesic in patients with chronic pain, while buccal buprenorphine is also a promising formulation based on the limited number of studies.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".