Acupuncture and reflexology for patients undergoing chemotherapy: an observational study
Bibliographic record
Abstract
Abstract Purpose Around three quarters of individuals undergoing chemotherapy self-report multiple symptoms. There is clinical trial evidence of effectiveness for acupuncture for commonly experienced symptoms, and emerging evidence for reflexology, but little is known about the effects of these therapies on multiple symptoms when implemented in a real world setting during active chemotherapy treatment. Methods This was a single-arm observational study of participants receiving reflexology and/or acupuncture while attending chemotherapy. Participants received a 20 minute reflexology treatment or a 20 minute acupuncture treatment or a combination of both. Patient reported outcome measures were administered before and after the treatment using the Edmonton Symptom Assessment Scale (ESAS). Results During the study period, 330 unique participants with cancer received acupuncture and/or reflexology treatments. Participants had, on average, 5.3 symptoms each which they reported as moderate to severe (≥4/10) using the ESAS at baseline. Following treatment, participants reported 3.2 symptoms as moderate to severe. The symptom change for all participant encounters receiving any therapy was statistically significant for all symptoms, and clinically significant (a reduction of more than 1) for all symptoms except financial distress, appetite and memory. Clinically significant levels of global distress (<3) were reduced in 72% of all participants receiving either therapy. No adverse events were recorded. Conclusions The results indicate that acupuncture and reflexology administered alongside chemotherapy may reduce patient reported symptom burden and patient global symptom related distress. Future research would include an active control group, and consider confounding factors such as chemotherapy stage and medication.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".