Guidelines for Cancer‐Related Pain: A Systematic Review of Complementary and Alternative Medicine Recommendations
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Although up to 85% of patients with cancer use complementary and alternative medicine (CAM), they commonly do not disclose this information to their healthcare providers. Cancer-related pain (CRP) is one of the most common symptoms among those who may seek CAM. This study was conducted to identify the quantity and assess the quality of CAM recommendations across clinical practice guidelines (CPGs) for the treatment and/or management of CRP, as this has not been explored in the literature. METHODS: A systematic review was conducted to identify cancer pain CPGs. MEDLINE, EMBASE, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched from 2009 to 2020. The Guideline International Network and the National Centre for Complementary and Integrative Health websites were also searched. Eligible CPGs on CRP in adults were assessed using the Appraisal of Guidelines, Research and Evaluation II (AGREE II) instrument. RESULTS: Of 771 unique search results, 13 mentioned CAM and 11 made CAM recommendations. Eligible CPGs were published in 2009 or later and focused on the treatment/management of CRP. Scaled domain percentages from highest to lowest ranged from (overall, CAM): 88.1%, 88.1% (for scope and purpose) to 21.0%, 8.5% (for applicability). Quality varied within and across CPGs. One CPG was recommended by both appraisers; 6 were recommended as "Yes" or "Yes with modifications." CONCLUSIONS: The present study has identified and summarized a number of CPGs that clinicians may consult to understand what CAMs are recommended in the context of the treatment and/or management of CRP.
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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 | 0.025 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".