Can Ultrasound of the Tibial Nerve Detect Diabetic Peripheral Neuropathy?
Bibliographic record
Abstract
OBJECTIVE: Peripheral nerve imaging by portable ultrasound (US) may serve as a noninvasive and lower-cost alternative to nerve conduction studies (NCS) for diagnosis and staging of diabetic sensorimotor polyneuropathy (DSP). We aimed to examine the association between the size of the posterior tibial nerve (PTN) and the presence and severity of DSP. RESEARCH DESIGN AND METHODS: We performed a cross-sectional study of 98 consecutive diabetic patients classified by NCS as subjects with DSP or control subjects. Severity was determined using the Toronto Clinical Neuropathy Score. A masked expert sonographer measured the cross-sectional area (CSA) of the PTN at 1, 3, and 5 cm proximal to the medial malleolus. RESULTS: Fifty-five patients had DSP. The mean CSA of the PTN in DSP compared with control subjects at distances of 1 (23.03 vs. 17.72 mm(2); P = 0.004), 3 (22.59 vs. 17.69 mm(2); P < 0.0001), and 5 cm (22.05 vs. 17.25 mm(2); P = 0.0005) proximal to the medial malleolus was significantly larger. Although the area under the curve (AUC) for CSA measurements at all three anatomical levels was similar, the CSA measured at 3 cm above the medial malleolus had an optimal threshold value for identification of DSP (19.01 mm(2)) with a sensitivity of 0.69 and a specificity of 0.77 by AUC analysis. CONCLUSIONS: This large study of diabetic patients confirms that the CSA of the PTN is larger in patients with DSP than in control subjects, and US is a promising point-of-care screening tool for DSP.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".