Nonopioid drug combinations for cancer pain: protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Pain related to cancer, and its treatment, is common, may severely impair quality of life, and imposes a burden on patients, their families and caregivers, and society. Cancer-related pain is often challenging to manage, with limitations of analgesic drugs including incomplete efficacy and dose-related adverse effects. OBJECTIVES: Given problems with, and limitations of, opioid use for cancer-related pain, the identification of nonopioid treatment strategies that could improve cancer pain care is an attractive concept. The hypothesis that combinations of mechanistically distinct analgesic drugs could provide superior analgesia and/or fewer adverse effects has been tested in several pain conditions, including in cancer-related pain. Here, we propose to review trials of nonopioid analgesic combinations for cancer-related pain. METHODS: Using a predefined literature search strategy, trials-comparing the combination of 2 or more nonopioid analgesics with at least one of the combination's individual components-will be searched on the PubMed and EMBASE databases from their inception until the date the searches are run. Outcomes will include pain intensity or relief, adverse effects, and concomitant opioid consumption. RESULTS/CONCLUSIONS: This review is expected to synthesize available evidence describing the efficacy and safety of nonopioid analgesic combinations for cancer-related pain. Furthermore, a review of this literature will serve to identify future research goals that would advance our knowledge in this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".