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Record W4205112177 · doi:10.2147/dmso.s340111

Median Nerve Affection in Hypertensive Patients with and without Diabetes High-Resolution Ultrasound Assessment

2022· article· en· W4205112177 on OpenAlexaboutno aff

Bibliographic record

VenueDiabetes Metabolic Syndrome and Obesity · 2022
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsDiabetes mellitusUltrasoundMedian nerveType 1 diabetes

Abstract

fetched live from OpenAlex

PURPOSE: Diabetes is a documented risk factor for peripheral neuropathy. It was reported that associated hypertension could increase this risk. The present study aimed to assess the effect of hypertension and diabetes on median nerve using high-resolution ultrasound. METHODS: The study includes 50 hypertensive patients (HTN group), 50 diabetic patients (DM group), 50 patients with coexisting diabetes and hypertension (HTN + DM group) and 50 healthy controls. Median nerve affection in the studied groups was studied by vibration perception thresholds (VPT). The median nerve cross-sectional area was determined at the nerve cross-sectional area of the median nerve at the carpal tunnel by high-resolution ultrasound. Clinical symptoms were assessed using Toronto Clinical Severity Score (TCSS). RESULTS: There was significantly higher median nerve CSA in all patient groups in comparison to controls. HTN + DM group had significantly higher median nerve CSA when compared with DM group. Patients with peripheral neuropathy in HTN + DM and DM groups had significantly higher median nerve CSA than patients without. Using ROC curve analysis, it was shown that median CSA could successfully distinguish patients with peripheral neuropathy from patients without in HTN + DM group [AUC (95% CI): 0.71 (0.54-0.89)] and in DM group [AUC (95% CI): 0.86 (0.72-0.99)]. CONCLUSION: Hypertensive patients with and without diabetes have significantly higher median nerve CSA when compared with controls.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.214
Teacher spread0.208 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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