Significance of high-resolution ultrasound imaging and elastography as early predictors of diabetic peripheral neuropathy
Bibliographic record
Abstract
Abstract To evaluate the significance of high-resolution ultrasound (HRUS) and shear wave elastography (SWE) in the diagnosis of diabetic peripheral neuropathy (DPN) to clarify their possible roles as early predictors of the occurrence of this important complication. The study included 90 patients with diabetes mellitus with different clinical stages of DPN as well as 30 healthy controls. A full history, clinical examination, and assessment of both the Toronto Clinical Neuropathy Score (TCNS) and HbA1c were performed, followed by real-time HRUS and SWE examinations of their right and left tibial and median nerves to assess their cross-sectional area (CSA) and nerve stiffness, respectively. The CSA and stiffness of tibial and median nerves were significantly increased in patients with diabetes compared to controls, with higher values associated with the severity of their DPN. Both parameters were correlated with each other and with the duration of the disease, TCNS, and HbA1c. The CSA cut-off value of both tibial and median nerves to detect DPN in patients was 13.5 mm2, meanwhile, the SWE cut-off values were 68.5 and 61.5 KPa, respectively. SWE showed a higher AUC than CSA for the prediction of DPN. Measurement of the CSA and stiffness of the peripheral nerves could be a reliable tool for early detection of DPN. Therefore, we recommend adding these noninvasive diagnostic parameters as complementary diagnostic tools to the routine follow-up schedule of diabetic complications, especially in long-standing cases.
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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 | 0.001 | 0.003 |
| 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".