Traditional Chinese Medicine Treating Dilated Cardiomyopathy: A Literature Review
Bibliographic record
Abstract
Hua Fan, Mengjiao Ma, Longping Peng, Feifei Liu, Tianyi Feng, Youhua Wang Cardiovascular Department, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of ChinaCorrespondence: Youhua Wang, Email doctorwyh@163.comAbstract: Dilated cardiomyopathy (DCM), as a difficult problem in modern medical treatment, has become an important cardiovascular disease threatening human beings all over the world with an increasing incidence rate and mortality. Conservative drug therapy is mainly used in clinical practice, but due to unavoidable adverse reactions such as low blood pressure, it is often difficult to achieve satisfactory prognosis. Traditional Chinese medicine has the characteristics of syndrome differentiation and multi-target treatment for DCM, with few adverse reactions and certain advantages. It has achieved good therapeutic effects in clinical practice. Therefore, we summarized and analyzed the clinical evidence and mechanism of traditional Chinese medicine in the treatment of DCM, and combined with the current research status of this disease to analyze the problems and shortcomings, in order to provide more ideas and methods for the treatment of DCM with traditional Chinese medicine.Keywords: dilated cardiomyopathy, traditional Chinese medicine, clinical evidence, potential mechanisms, research progress
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".