Retrospective analysis of outpatient acupuncture treatments at MD Anderson Cancer Center.
Bibliographic record
Abstract
e20681 Background: Cancer patients commonly use some form of complementary and alternative medicine, including acupuncture, during cancer treatment; however, there is limited available data about the impact of acupuncture treatment provided in the outpatient oncology clinical setting. Methods: Patients receiving acupuncture from June 2011 through December 2013 were asked to complete a modified Edmonton Symptom Assessment Scale (ESAS; 0-10 scale) before and after each visit. ESAS subscales analyzed included Physical Distress (PHS; range 0-70) and Psychological Distress (PSS; range 0-30). Data were analyzed examining the pre- and post-scores using paired t-tests. Results: A total of 3,915 (664 initial; 3,251 follow-up) acupuncture treatments were provided to 680 patients (average 5.8 visits/patient). The modified ESAS was completed for 2,836 visits (72.4%). The highest rated symptoms reported by all patients were fatigue 2.9 (±2.5), neuropathy 2.8 (±2.8), poor sleep 2.8 (±2.6), and pain 2.5 (±2.5). Symptoms with the greatest reduction in scores from before to after acupuncture included: fatigue (-1.0), pain (-0.8), neuropathy (-0.7), and poor sleep (-0.7), all with p-values <0.0001. There was a significant reduction from pre- to post-treatment in general well-being (-0.7) and in total symptom scores (mean [SD]: 19.4 [15.5] vs. 13 [12.9]; p<0.0001; N=1744). Both the PHS subscale (total of pain, fatigue, nausea, drowsiness, appetite, shortness of breath, and sleep) and the PSS subscale (total of anxiety, depression, and general well-being) demonstrated an improvement of 33% after acupuncture treatment. Conclusions: Patients receiving acupuncture treatment at a major cancer center commonly reported symptoms of fatigue, neuropathy, poor sleep and pain with some immediate improvement in symptoms after treatment. Acupuncture appears to have both physical and psychological benefits for patients. Further research is need to identify which patients are most likely to benefit from acupuncture as an adjunct to cancer treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".