High-dose Asian ginseng (Panax ginseng) for cancer-related fatigue (CRF): A preliminary report.
Bibliographic record
Abstract
9642 Background: CRF is a common and severe symptom in patients with cancer. There are limited useful treatments available. The objective of this preliminary study was to assess the safety of high-dose Panax ginseng (PG) on CRF. Methods: In this prospective open labeled study, 30 patients with cancer and fatigue ≥4/10 (0=no fatigue, 10=worst possible fatigue) received high dose PG 800mg orally daily for 29 days. Functional Assessment of Cancer Therapy-fatigue (FACIT-F), Edmonton Symptom Assessment System (ESAS) (0=best, 10=worst), and Hospital Anxiety Depression Scale (HADS) were assessed at baseline and day 29. Results: 24/30 (80%) patients were evaluable. The median age was 58yrs, 50% were females, 84% were white. The most common cancer type was genitourinary cancer (31%). Table shows the changes in fatigue, anxiety, depression scores. ESAS well-being improved from 4.67 (2.04) to 3.50 (2.34) (p=0.01374), appetite improved from 4.29 (2.79) to 2.96 (2.46) (p=0.0097). 21/24 (87%) patients had an improved FACIT-F fatigue score by day 15. Global Symptom Evaluation score of PG for fatigue was better in 15/24 patients (63%) with median improvement of 5 (1=hardly any better, 7= very great deal better). No ≥ grade 3 adverse events related to the study drug were reported. Conclusions: 1) PG is safe and rapidly improved ESAS fatigue and FACIT-F fatigue scores; 2) Overall quality of life (FACIT-General), appetite, and sleep at night also improved. Randomized controlled trials of PG are justified in CRF. Clinical trial information: NCT01375114. [Table: see text]
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".