Efficacy and safety of Shenkang injection in the treatment of chronic renal failure
Bibliographic record
Abstract
BACKGROUND: Chronic renal failure (CRF) is the final outcome of the development of multiple kidney diseases, and there is no effective method at home and abroad. Traditional Chinese medicine is found to play a major role in the treatment of the non-replacement stage of CRF. Shenkang injection can not only nourish the kidney, but also promote blood circulation and remove blood stasis, which is suitable for the treatment of CRF. This study aims to explore the efficacy and safety of Shenkang injection for CRF and provide evidence for clinical practice. METHODS: This was a prospective randomized controlled trial. One hundred four patients with CRF were randomly divided into treatment groups and control groups according to 1:1, with 52 patients in each group. The control group received basic treatment of western medicine and the treatment group was given Shenkang injection intravenously on the basis of control group. Both groups were given standard treatment for 4 weeks with concurrent follow-up for 1 month. The outcome indicators included: total efficiency, symptom scores, creatinine clearance rate, serum creatinine, blood urea nitrogen, CystatinC, liver function, blood routine, urine routine, incidence of adverse reactions, etc. Data analysis was performed using SPSS 25.0 software. DISCUSSION: This study will evaluate the efficacy and safety of Shenkang injection for CRF, and the results of this trial will provide clinical evidence for the treatment of CRF. TRIAL REGISTRATION: OSF Registration number: DOI 10.17605/OSF.IO/K9C5T.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".