Efficacy and safety of Zhen Wu Decoction against chronic heart failure
Bibliographic record
Abstract
INTRODUCTION: Chronic heart failure (CHF) is the end stage of several cardiac diseases. 50% of patients with severe CHF would survive less than 1 year, which has seriously affected patients' survival and quality of life. The current modern therapy could improve survival and quality of life. However, a larger number of patients still suffer from repeated hospitalization, unsatisfactory efficacy, and many side effects. In China, Zhen Wu Decoction (ZWD), a classic prescription recorded in Treatise on Febrile Diseases, was widely used for CHF. In this study, we try to conduct a prospective, double-blinded, randomized, controlled study to evaluate the efficacy and safety of ZWD in the treatment of CHF patients in China. METHODS: Patients will be randomly divided into treatment group and control group in 1:1 ratio. Guideline directed medical therapies and ZWD will be provided for patients in treatment group, while guideline directed medical therapies and ZWD-granules simulations for control group. Left ventricular ejection fraction, left ventricular end diastolic diameter, left ventricular end systolic diameter, b-type natriuretic peptide, NT-proBNP, peak VO2, VO2 maximum, exercise time, and walking distance will be recorded. The data will be analyzed by SPSS 22.0. CONCLUSIONS: The results will evaluate the efficacy and safety of ZWD in the treatment of CHF patients. TRIAL REGISTRATION: OSF registration number: DOI 10.17605/OSF.IO/G3QNU.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".