Exploring the Safety, Effectiveness, and Cost-Effectiveness of a Chinese Patent Medicine (Fufang E’jiao Syrup) for Alleviating Cancer-Related Fatigue: A Protocol for a Randomized, Double-Blinded, Placebo-Controlled, Multicenter Trial
Bibliographic record
Abstract
OBJECTIVE: To provide higher level evidence on the benefits of a Chinese patent medicine (CPM) (Fufang E'jiao Syrup, FFEJS) for alleviating cancer-related fatigue (CRF), this article describes a protocol for a randomized controlled trial. METHODS/DESIGN: We designed a double-blind, placebo-controlled stratified permuted block randomization clinical trial on CRF among 3 types of cancer in China. Participants will be equally allocated to FFEJS group or placebo group according to the randomization sequence and the hospitals they were enrolled at. Each patient will receive 20 ml of either the study formula FFEJS or a placebo formula, 3 times a day for 6 weeks. The follow-up period will be another 4 weeks for safety evaluation. The primary outcome is the difference in improvement of fatigue as measured with the Revised Piper Fatigue Scale-Chinese Version (RPFS-CV). Secondary outcomes include change in fatigue (measured by routine blood panel and hormones in peripheral blood) and QoL (measured by Edmonton symptom assessment scale and Functional Assessment of Cancer Therapy). Patient safety will be measured by liver, renal or cardiac damage, and the risk of FFEJS having a tumor promotion and progression effect will be monitored throughout this study. Cost-effectiveness will also be evaluated mainly by incremental cost per each quality-adjusted life year gained. DISCUSSION: This article describes the study design of a CPM for CRF in patients with advanced cancer through exploring the effectiveness, safety, and cost-effectiveness of FFEJS. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04147312. Registered on 1 Sep 2019.
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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 | 0.070 | 0.057 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".