Comparing Massage, Acupressure, and Combined Therapy for Managing Cancer-related Pain, Fatigue, and Sleep Disturbance: A 2 × 2 Factorial Randomized Controlled Trial
Bibliographic record
Abstract
Background: Massage and acupressure are highly popular among cancer patients as non-invasive methods with different mechanisms that can target multiple coexisting symptoms. However, the comparative effectiveness between these two techniques is still unclear, particularly among patients with advanced cancer. Furthermore, it is unclear whether both approaches would have a synergistic effect if applied simultaneously. The main objective is to compare the efficacy of massage alone versus acupressure alone versus combined therapy (massage plus acupressure) for managing cancer-related symptom cluster of fatigue, pain, and sleep disturbance. Methods: The 2-FAM-2 study is a four-arm, longitudinal, randomized trial comparing the efficacy of two complementary medicine techniques (massage alone vs. acupressure alone vs. combined therapy vs. control) for managing a fatigue-pain-sleep symptom cluster in patients with advanced cancer that will use a 2 × 2 factorial design with an equal allocation ratio. One hundred adult patients with advanced cancer who have all three symptoms of pain, fatigue, and sleep disturbance will be recruited. Four weeks of intervention and 4 weeks of follow-up with repeated measures will be part of the 8-week study period. The main outcome is the longitudinal trajectory (trend) in the intensity of the pain-sleep-fatigue symptom cluster over time, assessed via generalized estimating equations (GEE) at baseline, weekly during intervention (weeks 1-4), and at weeks 6 and 8 as follow-up. Self-reported fatigue, sleep disturbance, and pain items (0-10 scale) will be averaged to compute the symptom cluster intensity (SCI). Furthermore, a machine learning technique based on decision tree algorithms will be carried out to conduct a subgroup analysis aimed at predicting clinical outcomes for different interventions in homogeneous subgroups. Discussion: The trial's findings could be helpful in the development of clinical guidelines, individualization of intervention, as well as guiding clinical decisions and improving the quality of life of patients.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".