Buprenorphine for Cancer Pain: Results from a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Buprenorphine may be safer and better-tolerated than full mu opioid receptor (MOR) agonists. Whether it effectively controls cancer-related pain is unclear. A prior review (Cochrane 2015) did not support prioritizing buprenorphine over full MOR agonists for cancer-associated pain. PURPOSE/HYPOTHESIS: We conducted an updated systematic review of buprenorphine's effect on cancer- related pain including both new studies and additional study designs. Procedures/data/observations: We searched Cochrane, OVID Medline, EMBASE, EBSCO and Web of Science for studies published in any language up to May 2023 for studies that examined buprenorphine's impact upon pain severity/intensity in patients with active cancer. Risk of bias and study quality were assessed using the Cochrane Collaboration tool for randomized controlled trials (RCTs), and the Newcastle-Ottawa Scale for cohort and casecontrol studies. Data were synthesized using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria. CONCLUSIONS/APPLICATIONS: 2322 publications were identified and 42 studies were included (14 RCTs, 10 pre-post uncontrolled, 5 cohort, and 2 case-control studies). All had moderate-high risk of bias. One RCT showed buprenorphine was superior to placebo. 11 RCTs (12 papers) showed buprenorphine was as effective as full MOR agonists for cancer pain. 10-30 percent of cancer patients trialing buprenorphine did not achieve adequate response.
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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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".