Complementary Strategies for the Management of Radiation Therapy Side Effects
Bibliographic record
Abstract
Patients with cancer utilize complementary and alternative medicine (CAM) for a variety of purposes, one of which is the reduction of side effects of conventional treatment. With a large number of their patients using CAM, it is important for advanced practitioners in oncology to have an understanding of these therapies to better guide their patients. Side effects of radiation therapy that may have dose-limiting poten-tial include diarrhea, mucositis, skin toxicity, and xerostomia. A com-mon side effect that is not necessarily dose-limiting but considerably troublesome to patients is cancer- and treatment-related fatigue. The CAM therapies that may alleviate some of the side effects of radiation therapy include probiotics, psyllium, exercise, melatonin, honey, acu-puncture, and calendula. Therapies that require more research or have been shown to be ineffective include aloe vera, glutamine, and deglyc-yrrhizinated licorice. This article provides an overview of these thera-pies as well as related research and analysis.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".