Acupuncture for Cancer-Related Pain: An Open Clinical Trial
Bibliographic record
Abstract
Abstract Background: Among patients with cancer, pain is common and significantly impairs quality of life. The mainstay of treatment, opioids, can increase nausea and fatigue. Acupuncture appears to be efficacious for nonmalignant pain states. Studies of acupuncture for cancer-related pain are mixed, with systematic reviewers calling for further studies. Materials and Methods: Fifty-seven patients receiving treatment at a university oncology center and who had significant pain were seen in an open treatment program. A semistructured acupuncture protocol was designed to target pain, as well as anxiety, depression, fatigue, and nausea. Outcome measures included the Brief Pain Inventory (BPI) as the primary outcome and Edmonton Symptom Assessment System (ESAS) ratings of current symptoms. Patients were offered up to 12 sessions of acupuncture, typically over a 3-month period. Results: Twenty-five patients were considered to be treatment completers, receiving 9 or more sessions of acupuncture, and the analysis examines the response for these patients. Pain severity on the BPI decreased by 32% from baseline to the last session and pain interference decreased by 40%. Current symptoms on the ESAS decreased by ∼50% for pain, nausea, and fatigue, and by 44% for anxiety. Except for nausea, these change scores were all found to be significant on paired t -tests. Conclusions: This semistructured acupuncture protocol appeared to be effective for reducing cancer-related pain and other symptoms. Further study with a larger sample size, an appropriate control, and adequate follow-up is warranted. It would also be helpful to assess pragmatic outcomes including nausea and pain medication use and hospital admission for pain.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".