Is There a Role for Acupuncture in the Symptom Management of Patients Receiving Palliative Care for Cancer? A Pilot Study of 20 Patients Comparing Acupuncture with Nurse-Led Supportive Care
Bibliographic record
Abstract
PURPOSE: A pilot study to document changes in symptoms after acupuncture or nurse-led supportive care in patients with incurable cancer. METHODS: Patients receiving palliative care with estimated survival of at least 3 months were screened with the Edmonton Symptom Assessment System (ESAS). Patients (n=20) with significant symptoms were randomised to receive weekly acupuncture or nurse-led supportive care for 4 weeks. ESAS scores were obtained before and after each treatment, and weekly for 6 weeks after treatment by telephone. RESULTS: 42 of 170 patients screened were eligible. 20 gave consent for recruitment. The compliance rate was 90% for acupuncture and 80% for nurse-led supportive care. Total symptom scores were reduced by an average of 22% after each acupuncture visit and by 14% after each supportive care visit. Compared with baseline, ESAS scores at the end of the follow-up period were reduced by 19% for the acupuncture arm and 26% for nurse-led supportive care. CONCLUSION: Patients appear to benefit from incorporating acupuncture in the treatment of advanced incurable cancer. Acupuncture was well tolerated with no significant or unexpected side effects. Acupuncture had an immediate effect on all symptoms, whereas nurse-led supportive care had a larger impact 6 weeks after the final session. Both interventions appear helpful to this population and warrant further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".