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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".