Integrated Oncology in an Integrated Medicine Hospital in Pitigliano (Grosseto, Italy)
Bibliographic record
Abstract
Background: Complementary medicines (CM), including homeopathy, acupuncture, and traditional Chinese medicine, have been introduced for cancer patients undergoing chemotherapy and radiotherapy treatment in the Pitigliano Hospital Centre of Integrated Medicine in order to minimize the side effects of these treatments, which improves quality of life and adherence to conventional therapies. Methods: Cancer patients (240) were enrolled in an integrated care model offering a comprehensive protocol including homeopathy and acupuncture, provided in line with the stage of the disease as well as in consideration of any comorbidities in individual patients. The following data were collected upon enrollment and also after 1-2 months of the integrated therapies:SF-12 quality of life (QoL) questionnaire;Edmonton symptom assessment scale (ESAS);and a questionnaire on the use of conventional medications. Results: There was a 92.4% reduction in symptoms (as monitored by ESAS) caused by the patient’s disease or by comorbidities. The SF-12 revealed reduced fatigue and increased wellness, as well as good adherence to the cancer treatments. Additionally, a reduction in the use of conventional medications for side effects, good control of cancer symptoms, and an improved QoL was also observed. Conclusions: This study suggests the use of complementary medicines to reduce therapy-related symptoms in oncologic patients without any adverse effects and to reduce the use of conventional drugs in this case.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".