Design and Methodology of a Multicenter Randomized Clinical Trial to Evaluate the Efficacy of Tongmai Jiangtang Capsules in Type 2 Diabetic Coronary Heart Disease Patients
Bibliographic record
Abstract
Background: Population-based studies have consistently showed an increased incidence of coronary heart disease and cardiac mortality in patients with type 2 diabetes mellitus (T2DM). Tongmai Jiangtang capsules (TJC) are Chinese patent medicines that have been approved in China for the treatment of diabetic vascular complications. However, the evidence supporting the efficacy of Tongmai Jiangtang capsules in type 2 diabetic coronary heart disease (T2DM-CHD) remains unclear. Herein, we designed a randomized, parallel-controlled clinical trial to investigate a new complementary therapy for T2DM-CHD patients. Methods: A total of 360 T2DM-CHD subjects (aged 18–75 years) will be randomly assigned to the TJC group or the placebo group at a 2:1 ratio. On the basis of western medicine therapy, all the participants will receive TJC or placebo, orally, three capsules/treatment, three per day for 12 weeks. The primary outcomes will be assessed according to the Canadian Cardiovascular Society (CCS) classification. All statistical analyses will be performed setting a two-sided 0.05 significance level, using SAS 9.4 statistical software. Discussion: The efficacy of TJC for the treatment of T2DM-CHD patients will be evaluated. The study will provide reliable clinical research evidence for application of TJC in treating T2DM-CHD patients. Clinical Trial Registration: https://www.chictr.org.cn/enIndex.aspx , Chinese Clinical Trial Registry ChiCTR2000037491.
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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 | 0.049 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".