Moxibustion for cancer-related fatigue: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Cancer-related fatigue is one of the most common symptoms experienced by cancer patients, and it diminishes their quality of life. However, there is currently no confirmed standard treatment for cancer-related fatigue, and thus, many patients who suffer cancer-related fatigue seek complementary and alternative medicines such as moxibustion. Moxibustion is one of the most popular therapies in traditional Korean medicine used to manage fatigue. Recent studies have also demonstrated that moxibustion is effective for treating chronic fatigue. However, there is insufficient evidence supporting the effect of moxibustion against cancer-related fatigue. The aim of this study is to assess the efficacy and safety of moxibustion treatment for cancer-related fatigue. METHODS/DESIGN: A multi-center, three-armed parallel, randomized controlled trial will be conducted. Ninety-six patients with cancer-related fatigue will be recruited from three clinical research centers. They will be randomly allocated to one of three groups in a 1:1:1 ratio. The moxibustion group will receive moxibustion treatment at CV8, CV12, LI4 and ST36. The sham moxibustion group will receive sham moxibustion at non-acupoints. Both the moxibustion and sham moxibustion groups will receive 30-min treatments twice a week for 8 weeks. The usual care group will not receive moxibustion treatment. All participants will be educated via a brochure on how to manage cancer-related fatigue in daily life. The outcome measurements will be evaluated at baseline, week 5, week 9, and week 13 by assessors who are blinded to the group allocation. The primary outcome measure will be the mean change in the average scores of the Brief Fatigue Inventory before and after treatments between groups. The secondary outcome measures will be the mean difference in changes from baseline of the Brief Fatigue Inventory, functional assessments of cancer therapy-fatigue, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C-30 scores, and Montreal Cognitive Assessment scores between groups. Safety will be assessed by monitoring adverse events at each visit. DISCUSSION: The results of this study will provide evidence to confirm whether moxibustion can be used as a therapeutic option for treating cancer-related fatigue. TRIAL REGISTRATION: Clinical Research Information Service KCT0002170 . Registered 16 December 2016.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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) | 0.001 | 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".