Evidence map›Paper›PMID 35495881›Full record

Trial reportComputational and mathematical methods in medicine2022

Clinical Application of the Classical Theory of Traditional Chinese Medicine in Diabetic Nephropathy.

Jintong Pan, Huihui Li, Junhua Shi

RetractedAbstract readRandomized Controlled TrialRetracted Publication
In one paragraph

Trial report in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Jintong PanHubei Province Hospital of Traditional Chinese Medicine, Nephropathy, China.
Huihui LiGuangzhou Weimi Biological Technology Company, China.
Junhua ShiHubei Province Hospital of Traditional Chinese Medicine, Nephropathy, China.ORCID https://orcid.org/0000-0003-1371-5235

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To explore the clinical application of the classical theory of traditional Chinese medicine (TCM) in diabetic nephropathy (DN). Methods: A total of 100 patients with DN treated in our hospital from May 2019 to June 2021 were included. The patients were randomly assigned to the control group and the study group. The control group received routine treatment, and the study group was treated with the classical theory of TCM. The efficacy, TCM syndrome score, urine proteinuria (UTP), urine albumin-creatinine ratio (UACR), plasma albumin (ALB), hemoglobin A1c (HbA1C), fasting blood glucose (FBG), blood urea nitrogen (BUN), creatinine (Cr), and treatment safety were compared between the two groups. Results: In comparison to the curative effect, the study group was significantly effective in 34 cases, effective in 12 cases, and ineffective in 4 cases, and the effective rate was 92.00%; the control group was significantly effective in 16 cases, effective in 18 cases, and ineffective in 16 cases, and the effective rate was 68.00%. The effective rate in the study group was higher compared to the control ( Conclusion: Under the guidance of classical theory, the optimization scheme of comprehensive treatment of TCM may improve renal function by improving metabolic disorders, vascular lesions, neurotrophic disorders, antioxidant stress, and other ways to repair nerve injury, improving the changes of TCM syndromes, signs, and indicators of patients, and delay the progress of DN.

Indexed as

Diabetes MellitusDiabetic NephropathiesCreatinineFemaleGlycated HemoglobinHumansMaleMedicine, Chinese TraditionalSyndromeUridine TriphosphateCreatinineGlycated HemoglobinUridine Triphosphate

Identifiers

PMID35495881
PMCPMC9045989

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.