Median Nerve Affection in Hypertensive Patients with and without Diabetes High-Resolution Ultrasound Assessment
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".