Diagnostic accuracy of nerve ultrasonography for the detection of peripheral neuropathy in type 2 diabetes
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Nerve conduction studies (NCS) are the current objective measure for diagnosis of peripheral neuropathy in type 2 diabetes but do not assess nerve structure. This study investigated the utility of peripheral nerve ultrasound as a marker of the presence and severity of peripheral neuropathy in type 2 diabetes. METHODS: A total of 156 patients were recruited, and nerve ultrasound was undertaken on distal tibial and distal median nerves. Neuropathy severity was graded using the modified Toronto Clinical Neuropathy Scale (mTCNS) and Total Neuropathy Score (TNS). Studies were undertaken by a single ultrasonographer blinded to nerve conduction results. RESULTS: A stepwise increase in tibial nerve cross-sectional area (CSA) was noted with increasing TNS grade (p < 0.001) and each mTCNS quartile (p < 0.001). Regression analysis demonstrated a correlation between tibial nerve CSA and neuropathy severity (p < 0.001). Using receiver operator curve analysis, tibial nerve CSA of >12.88 mm yielded a sensitivity of 70.5% and specificity of 85.7% for neuropathy detection. Binary logistic regression revealed that tibial nerve CSA was a predictor of abnormal sural sensory nerve action potential amplitude (odds ratio = 1.239, 95% confidence interval [CI] = 1.142-1.345) and abnormal neuropathy score (odds ratio = 1.537, 95% confidence interval [CI] = 1.286-1.838). CONCLUSIONS: Tibial nerve ultrasound has good specificity and sensitivity for neuropathy diagnosis in type 2 diabetes. The study demonstrates that tibial nerve CSA correlates with neuropathy severity. Future serial studies using both ultrasound and NCS may be useful in determining whether changes in ultrasound occur prior to development of nerve conduction abnormalities and neuropathic symptoms.
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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 | 0.004 | 0.024 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".