Outpatient acupuncture effects on patient self-reported symptoms in oncology care: A retrospective analysis of real-world data.
Bibliographic record
Abstract
135 Background: Increased access to complementary therapies such as acupuncture at academic medical centers has created new opportunities for management of cancer and cancer treatment related symptoms. Methods: Patients presenting for acupuncture treatment during calendar year 2016 at an outpatient integrative medicine clinic in a comprehensive cancer center were asked to complete a modified Edmonton Symptom Assessment Scale (ESAS; 16 symptoms, score 0-10, 10 worst possible) before and after each visit. ESAS subscales analyzed included global (GDS; score 0-90), physical (PHS, 0-60), and psychological distress (PSS, 0-20). ESAS symptom score change pre/post treatment and from baseline visit to first follow up were evaluated by paired t-test. Results: Of 375 participants [mean age 55.6, 68.3% female, 73.9% white, most common cancer diagnosis of breast (32.8%) and thoracic/head&neck (25.9%)], 73.3% had at least one follow-up treatment [mean 4.6 (SD 5.1) treatments]. Highest/worst symptoms at baseline were poor sleep (3.92), fatigue (3.43), well-being (3.31), and pain (3.29). Statistically significant reduction/improvement (pre/post) was observed for all ESAS symptoms and subscales for the initial treatment (p <0.001). Hot flashes had the highest mean reduction (-1.93), followed by fatigue (-1.72), numbness/tingling (-1.70), and nausea (-1.67). Clinically significant reductions were also observed for ESAS subscales of GDS (-12.2), PHS (-8.5), and PSS (-2.6). For symptom change from initial treatment to first follow-up (pre/pre), statistically and clinically significant improvement was observed for spiritual pain (-1.10; p<0.001) and ESAS subscale of GDS (-6.09; p=0.048). Clinical response rates (reduction ≥1) on follow-up were highest for symptoms of spiritual pain (58.9%), dry mouth (57.8%) and nausea (57.3%). Conclusions: Outpatient acupuncture was associated with immediate and longitudinal significant improvement across a range of symptoms commonly experienced by individuals during cancer care. Further research is needed to better understand frequency of treatments needed in clinical practice to help maintain benefit.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".