High-Dose Asian Ginseng (Panax Ginseng) for Cancer-Related Fatigue
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: Cancer-related fatigue (CRF) is the most common and severe symptom in patients with cancer. The number and efficacy of available treatments for CRF are limited. The objective of this preliminary study was to assess the safety of high-dose Panax ginseng (PG) for CRF. METHODS: In this prospective, open-label study, 30 patients with CRF (≥4/10) received high-dose PG at 800 mg orally daily for 29 days. Frequency and type of side effects were determined by the National Cancer Institute's Common Terminology Criteria for Adverse Events, version 4.0. Scores on the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scale, Edmonton Symptom Assessment System (ESAS), and Hospital Anxiety and Depression Scale (HADS) were assessed at baseline, day 15, and day 29. Global Symptom Evaluation (GSE) was assessed at day 29. RESULTS: Of the 30 patients enrolled, 24 (80%) were evaluable. The median age was 58 years; 50% were females, and 84% were white. No severe (≥grade 3) adverse events related to the study drug were reported. Of the 24 evaluable patients, 21 (87%) had an improved (by ≥3 points) FACIT-F score by day 15. The mean ESAS score (standard deviation) for well-being improved from 4.67 (2.04) to 3.50 (2.34) (P = .01374), and mean score for appetite improved from 4.29 (2.79) to 2.96 (2.46) (P = .0097). GSE score of PG for fatigue was ≥3 in 15/24 patients (63%) with median improvement of 5. CONCLUSION: PG is safe and improves CRF fatigue as well as overall quality of life, appetite, and sleep at night. Randomized controlled trials of PG for CRF are justified.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".