Acupuncture Improves Functional Limitations for Cancer Patients with Chronic Pain: A Secondary Analysis of PEACE Randomized Clinical Trial
Bibliographic record
Abstract
Chronic pain significantly impairs functional performance in patients with cancer. Although acupuncture is effective for cancer-related pain, its impact on pain-related functional interference remains unclear. This secondary analysis of the PEACE randomized clinical trial included patients with prior cancer diagnoses and musculoskeletal pain for ≥3 months. Participants were randomized to groups undergoing 10 weeks of electro-acupuncture, auricular acupuncture, or a waitlist control. Functional performance was assessed using the Quick-Disability Arm/Shoulder/Hand (Q-DASH) for upper limbs and the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) subscale for lower limbs (higher scores = worse function). Linear mixed models compared changes over time between groups, with week 12 as the primary endpoint. Functional changes were also compared between pain responders and non-responders in the acupuncture arms. Among 360 patients (mean [SD] age, 62.1 [12.7] years; 69.7% women), mean baseline Q-DASH and WOMAC scores were 33.2 (19.8) and 33.3 (20.3). At week 12, both electro-acupuncture and auricular acupuncture significantly improved function versus waitlist: Q-DASH by −7.18 and −9.64 points, respectively, and WOMAC by −6.89 and −7.61 points (all p < 0.001). No differences were found between the two acupuncture groups. Treatment effects on Q-DASH diminished during follow-up, while improvements on WOMAC persisted. Within the acupuncture groups, pain responders achieved greater functional gains than non-responders (Q-DASH, −6.74; WOMAC, −6.16; both p < 0.001). Electro-acupuncture and auricular acupuncture improved upper and lower extremity function in cancer patients with chronic pain. These findings support acupuncture as a potential adjunct in functional rehabilitation for cancer survivors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".