Bibliographic record
Abstract
Background: Acupuncture is an innovative and scientifically supported treatment for oncologic patients, as well as an effective palliative care option. At the Palliative Care Department of "Misericordia" Grosseto Hospital in Tuscany, real integration of acupuncture in palliative medicine has been possible. The objective of this work is to retrospectively evaluate patient treatment outcomes obtained using an integrated medical care approach administered within a palliative care unit. Methods: Medical records of oncology patients admitted to the palliative care unit who voluntarily underwent integrated therapy with acupuncture were retrospectively analyzed. Treatment was innovative and personalized and included the use of - Points based on Traditional Chinese Medicine (TCM) - Points based on Microsystems Acupuncture Technique - Points with a psychic action (Shen Ling) Codified evaluation indexes were used to rate patient status at the beginning of treatment and after 1-2 months. Treatment outcomes were evaluated for oncologic patients voluntarily participating as palliative care outpatients or inpatients receiving integrated acupuncture therapy. Results: 172 cancer patients treated over a two-year period received a total of approximately 600 treatments, with 92.4% of patients reporting improvement of symptoms. Examination of patient data obtained from the SF 12 questionnaire and of patient assessments based on the Edmonton Symptoms Assessment Scale (ESAS) revealed remarkable improvement in perceived health 2 months after initiation of integrated therapy p<0.01. Specifically, marked post-treatment improvement in symptoms of pain, fatigue, nausea, sleep disturbance, anxiety, loss of appetite, shortness of breath, cough and well-being were observed, with no improvement observed for dry mouth or depression. No major side effects were reported. Conclusions: Acupuncture is a promising and safe adjunctive therapy for management of common symptoms that afflict oncological patients during all stages of disease, including symptoms of patients nearing the end of life in a home or hospice setting.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".