Effects of high-dose Asian ginseng (Panax ginseng) to improve cancer-related fatigue: Results of a double-blind, placebo-controlled randomized controlled trial.
Bibliographic record
Abstract
209 Background: Cancer related fatigue (CRF) is the most common and disabling symptom in cancer.Panax ginseng extract (PG) is frequently used as a nutraceutical treatment for fatigue. There are no well-powered placebo-controlled trials that used validated CRF outcome measures to investigate of PG effects in cancer patients. The primary objective of this trial was to evaluate the effects oral PG versus Placebo (PL) for CRF. Methods: Patients with cancer fatigue ≥ 4/10 on Edmonton Symptom Assessment Scale (ESAS) were eligible. Patients were randomized to either 400mg of standardized PG or matching PL orally twice a day for 28 days. The primary endpoint was change in the Functional Assessment of Chronic Illness-Fatigue (FACIT-F) fatigue subscale from baseline to Day 28. Secondary outcomes were Functional Assessment of Cancer Therapy-General (FACT-G), Hospital Anxiety and Depression Scale (HADS), and ESAS. Results: Total evaluable patients were 112 (56 for PG and PL groups). No significant differences in baseline characteristics between the two groups except cancer type (p = 0.002). There was significant improvement in FACIT fatigue and ESAS fatigue scores in PG and PL groups at Day 15 and Day 29. The mean (SD) of FACIT-F fatigue scores at baseline, Day 15, and Day 29 were 22.6 (10.4), 29.8(10.7), 30.1 (11.6) and 23.8 (9.1), 30.0 (10.1), 30.4 (11.6) for PG and PL respectively. Mean (SD) improvement in the FACIT-F subscale at Day 29 was not significantly different in PG than in the PL group [7.5 (12.7) vs 6.5 (9.9), P = 0.67]. Mean (SD) improvement in the ESAS fatigue, FACT-G, and HADS at Day 29 were also not significantly different in PG than in the PL group. In a multiple linear model analysis, the change in FACIT-F fatigue from Day 0 to Day 29 was negatively correlated with baseline FACIT-F fatigue (p = 0.0005), baseline HADS score (p = 0.032), and male gender (p = 0.023). There were a significantly higher number of any grade of toxicities in PL group than in PG group (33/62 vs. 28/64, p = 0.024). Conclusions: Both PG and Placebo result in a significant improvement in CRF at Day 15 and Day 29. PG was not significantly superior to placebo after 4 weeks of treatment. Further studies are needed. Clinical trial information: NCT01375114.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".