Hot stone therapy in palliative care.
Bibliographic record
Abstract
179 Background: Massage therapy is a popular adjunct to cancer palliation. Hot Stone therapy, a massage method with hot lava stones, is an ancestral native Indian medicine practice. Since 2011, funds permit us to offer Hot Stone therapy as a new Complementary Medicine for inpatients in the oncology unit where Palliative Care is integrated early into oncologic care. We assessed the effect of Hot Stone therapy in patients with advanced cancer on symptom severity. Methods: The study took place at Hopital prive Oceane, a private setting in France, between May 1, 2014 and May 1 , 2015. After physician's check, one nurse who has training in stone therapy method provided massage. We assessed - overall patient's satisfaction - severity of nine common symptoms with Edmonton Symptom Assessment System (ESAS) before and after the massage. We reported - number of each symptom (%) experienced by patient before the massage - number of massages (%) with change score for analyses. Results: Of 156 massages, 113 were assessed (82 patients). Satisfaction rate is 100%. Rate of symptoms before massages: pain, 51%; fatigue, 88%; nausea, 21%; depression, 63%; anxiety, 79%; drowsiness , 72%; lack of appetite, 62%;dyspnea, 36%; no wellbeing, 93%. Decrease of 2 points in change score: pain (71%), fatigue (22%), nausea (25%), depression (42%), anxiety (64%), drowsiness (10%), dyspnea (37%), wellbeing (84%). None increase except in drowsiness (41%) which can be explained by more relaxed patients. No difference in lack of appetite. Conclusions: In our experience, Hot Stone therapy is a type of massage that help patient feel better. A decrease in some very common symptom in palliative care was observed. Stone therapy is a safe method and can be use by training caregivers to alleviate a wide range of cancer symptoms or side effects of treatment with a non-pharmacological approach. Actually, we develop a Hot Stone therapy program's training to offer more massages in our institution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".