Effectiveness of acupuncture for cancer pain: protocol for an umbrella review and meta-analyses of controlled trials
Bibliographic record
Abstract
INTRODUCTION: The National Comprehensive Cancer Network guidelines for adult cancer pain indicate that acupuncture and related therapies may be valuable additions to pharmacological interventions for pain management. Of the systematic reviews related to this topic, some concluded that acupuncture was promising for alleviating cancer pain, while others argued that the evidence was insufficient to support its effectiveness. METHODS AND ANALYSIS: This review will consist of three components: (1) synthesis of findings from existing systematic reviews; (2) updated meta-analyses of randomised clinical trials and (3) analyses of results of other types of clinical studies. We will search six English and four Chinese biomedical databases, dissertations and grey literature to identify systematic reviews and primary clinical studies. Two reviewers will screen results of the literature searches independently to identify included reviews and studies. Data from included articles will be abstracted for assessment, analysis and summary. Two assessors will appraise the quality of systematic reviews using Assessment of Multiple Systematic Reviews; assess the randomised controlled trials using the Cochrane Collaboration's risk of bias tool and other types of studies according to the Newcastle-Ottawa Scale. We will use 'summary of evidence' tables to present evidence from existing systematic reviews and meta-analyses. Using the primary clinical studies, we will conduct meta-analysis for each outcome, by grouping studies based on the type of acupuncture, the comparator and the specific type of pain. Sensitivity analyses are planned according to clinical factors, acupuncture method, methodological characteristics and presence of statistical heterogeneity as applicable. For the non-randomised studies, we will tabulate the characteristics, outcome measures and the reported results of each study. Consistencies and inconsistencies in evidence will be investigated and discussed. Finally, we will use the Grading of Recommendations Assessment, Development and Evaluation approach to evaluate the quality of the overall evidence. ETHICS AND DISSEMINATION: There are no ethical considerations associated with this review. The findings will be disseminated in peer-reviewed journals or conference presentations. PROSPERO REGISTRATION NUMBER: CRD42017064113.
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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 | 0.110 | 0.182 |
| Meta-epidemiology (narrow) | 0.011 | 0.009 |
| Meta-epidemiology (broad) | 0.029 | 0.042 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.081 | 0.015 |
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".