Evidence map›Paper›PMID 36404810›Full record

ArticleJournal of diabetes and metabolic disorders2022

Sensitivity and specificity of high frequency ultrasound score (DCEC) in diabetic peripheral neuropathy.

Hailun Huang, Chao Tang, Mi Li, Jing Huang, Yan Li, Shan Wu

Open access · hybridAbstract read
In one paragraph

Article in Journal of diabetes and metabolic disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 25% of its field
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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Hailun HuangDepartment of Neurology, Affiliated Hospital of Guizhou Medical University, 550001 Guiyang, China.
Chao TangDepartment of Neurology, Affiliated Hospital of Guizhou Medical University, 550001 Guiyang, China.
Mi LiDepartment of Neurology, Affiliated Hospital of Guizhou Medical University, 550001 Guiyang, China.
Jing HuangDepartment of Neurology, Affiliated Hospital of Guizhou Medical University, 550001 Guiyang, China.
Yan LiDepartment of Neurology, Affiliated Hospital of Guizhou Medical University, 550001 Guiyang, China.
Shan WuDepartment of Neuropathy, Affiliated Hospital of Guizhou Medical University, No. 28 of Guiyi Street, Yunyan District, 550004 Guiyang, China.
Affiliated Hospital of Guizhou Medical University · CNGuiyang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To summarize the ultrasonic characteristics of peripheral nerve damage in type 2 diabetes and to verify the diagnostic value of DCEC score for DPN. Methods: A total of 289 patients with type 2 diabetes evaluated peripheral neuropathy with neuroultrasound and nerve conduction at the Affiliated Hospital of Guizhou Medical University from June 2016 to June 2020. According to the diagnostic criteria from 2017 guidelines of China, 289 patients with type 2 diabetes were divided into three groups: DPN group: 203 cases; subclinical group: 48 cases; and non-DPN group: 38 cases. Kruskal Wallis test was used to identify the differences and characteristics of ultrasound scores between the all groups. The best cut-off value, sensitivity and specificity of DCEC score were obtained by receiver operator characteristic curve. Taking the diagnostic standard of diabetes peripheral neuropathy as the "gold standard", the best diagnostic threshold, sensitivity and specificity were obtained by drawing the ROC curve of DCEC score, and then the diagnostic value of DCEC score for DPN was verified. Results: Compared with non-DPN group, DCEC score in DPN group was significantly higher (P < 0.05). Otherwise,according to the ROC curve, the best cut-off value of DCEC score for DPN diagnosis was 12.5 (sensitivity 69.7%, specificity 71.1%). Conclusions: The DCEC score system can effectively diagnose DPN with length-dependence,mainly including the increase of definition score.

Identifiers

PMID36404810
PMCPMC9672188
OpenAlexW4289888790

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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.