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Record W4389939131 · doi:10.1155/2023/8897065

The Relationship between Muscle Ultrasound Parameters and Diabetic Peripheral Neuropathy among Patients with Type 2 Diabetes Mellitus: A Cross-Sectional Study

2023· article· en· W4389939131 on OpenAlexfundno aff
Yanling Zhong, Xiaojia Liu, Teng Lin

Bibliographic record

VenueJournal of Diabetes Research · 2023
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsMedicineCross-sectional studyDiabetes mellitusPeripheral neuropathyPeripheralInternal medicineType 2 Diabetes MellitusDiabetic neuropathyUltrasoundType 2 diabetesEndocrinologyPathologyRadiology

Abstract

fetched live from OpenAlex

Background. Muscle dysfunction is an early complication of diabetic peripheral neuropathy (DPN). As a convenient and low-cost tool, muscle ultrasound has been used to assess muscle quality and muscle mass. However, the relationship between different muscle ultrasound parameters and DPN is unclear. Objectives. This study was designed to investigate the relationship between ultrasound parameters of different muscles and DPN among patients with type 2 diabetes mellitus, including the rectus femoris (RF), tibialis anterior (TA), and medial head of gastrocnemius (MG). Materials and Methods. The research enrolled 90 patients with type 2 diabetes mellitus (T2DM). All images were attained from both sides. Muscle measurements contained muscle thickness (MT), cross-sectional area (CSA), echo intensity (EI), and corrected EI. The binary logistic regression and multiple linear regression were used to investigate the association between muscle ultrasound parameters and DPN or vibration perception threshold (VPT). Results. EI, corrected EI, MT of MG, and EI of TA were associated with DPN separately after adjusting other clinical variates. Among these muscle parameters, the EI of MG had a better predictive value (OR: 1.114, 95% CI: 1.039, 1.196) of DPN. Combined with CSA of RF, peripheral artery disease (PAD), and sex, the corrected EI of MG was associated with the vibration perception threshold (VPT) (standard <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>β</a:mi> <a:mo>=</a:mo> <a:mn>0.242</a:mn> </a:math> , <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>p</c:mi> <c:mo>&lt;</c:mo> <c:mn>0.001</c:mn> </c:math> ), better than the EI of MG (standard <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>β</e:mi> <e:mo>=</e:mo> <e:mn>0.215</e:mn> </e:math> , <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:mi>p</g:mi> <g:mo>=</g:mo> <g:mn>0.002</g:mn> </g:math> ). Conclusions. MG (MT, EI, and corrected EI) and TA (EI) were associated with DPN, respectively. CSA of RF and corrected EI or EI of MG combined with PAD and sex were associated with VPT significantly, which supported that muscle ultrasound might be a substantial quantitative tool for detecting the exercise benefits for DPN.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.113
GPT teacher head0.403
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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