Characteristics and Symptom Burden of Patients Accessing Acupuncture Services at a Cancer Hospital
Bibliographic record
Abstract
BACKGROUND: Patients with cancer are often impacted by a significant symptom burden. Cancer hospitals increasingly recognize the value of complementary and integrative therapies to support the management of cancer related symptoms. The aim of this study is to provide a better understanding of the demographic characteristics and symptoms experienced by cancer patients who access acupuncture services in a tertiary hospital in Australia. METHODS: A retrospective audit was conducted of patients that presented to the acupuncture service at Chris O'Brien Lifehouse between July 2017 and December 2018. Edmonton Symptom Assessment Scale (ESAS) and Measure Yourself Concerns and Wellbeing (MYCaW) outcome measures were used. The quantitative data was analyzed using descriptive statistics and Principal Component Analysis. RESULTS: A total of 127 inpatients and outpatients (mean age 55, range 19-85) were included with 441 individual surveys completed (264 ESAS, 177 MYCaW). Patients were predominantly female (76.8%) and breast cancer was the most prevalent primary diagnosis (48%). The most prevalent symptoms in the ESAS were sleep problems (88.6%), fatigue (88.3%), lack of wellbeing (88.1%), and memory difficulty (82.6%). Similarly, symptoms with the highest mean scores were numbness, fatigue, sleep problems and hot flushes, whilst neuropathy, and hot flashes were scored as the most severe (score ≥7) by patients. Cluster analysis yielded 3 symptom clusters, 2 included "physical symptoms" (pain, sleep problems, fatigue and numbness/neuropathy), and (nausea, appetite, general well-being), whilst the third included "psychological" symptoms (anxiety, depression, spiritual pain, financial distress). The most frequent concerns expressed by patients (MyCaW) seeking acupuncture were side effects of chemotherapy (24.6%) and pain (20.8%). CONCLUSION: This audit highlights the most prevalent symptoms, the symptoms with the greatest burden and the types of patients that receive acupuncture services at an Australian tertiary hospital setting. The findings of this audit provide direction for future acupuncture practices and research in hospital settings.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".