Nonopioid drug combinations for cancer pain: a systematic review
Bibliographic record
Abstract
Pain is highly prevalent in patients with cancer-nearly 40% report moderate-severe pain, which is commonly treated with opioids. Increasing cancer survivorship, opioid epidemics in some regions of the world, and limited opioid access in other regions have focused attention on nonopioid treatments. Given the limitations of monotherapy, combining nonopioids-such as antiepileptics and antidepressants-have shown promise in noncancer pain. This review seeks to evaluate efficacy of nonopioid combinations for cancer-related pain. Systematic searches of PubMed, EMBASE, and Cochrane CENTRAL were conducted for double-blind, randomized, controlled trials comparing a nonopioid combination with at least one of its components and/or placebo. This search yielded 4 randomized controlled trials, published between 1998 and 2019 involving studies of (1) imipramine + diclofenac; (2) mitoxantrone + prednisone + clodronate; (3) pentoxifylline + tocopherol + clodronate; and (4) duloxetine + pregabalin + opioid. In the first 3 of these trials, trends favouring combination efficacy failed to reach statistical significance. However, in the fourth trial, duloxetine + pregabalin + opioid was superior to pregabalin + opioid. This review illustrates recognition for the need to evaluate nonopioid drug combinations in cancer pain, although few trials have been published to date. Given the growing practice of prescribing more than 1 nonopioid for cancer pain and the need to expand the evidence base for rational combination therapy, more high-quality trials in this area are needed.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".