Role of High-Resolution Ultrasonography of Ulnar Nerve in the Evaluation of Diabetic Peripheral Neuropathy
Bibliographic record
Abstract
BACKGROUND \nDiabetic peripheral neuropathy is a long term complication of diabetes. \nTraditionally, clinical history, physical examination and electro physiological studies \nwere relied upon for diagnosis. Currently, High resolution ultrasonography has \ncome into picture in the diagnosis of peripheral neuropathy due to the ease, time \nsaving ability and noninvasiveness of the procedure. We wanted to correlate the \ncross-sectional area and maximum thickness of nerve fascicles of the ulnar nerve \nwith the presence and severity of diabetic peripheral neuropathy. \nMETHODS \nA retrospective study was conducted between October 2018 and January 2019. \nThe study group consisted of 85 type 2 diabetic patients. 55 Diabetic patients with \nclinical signs and symptoms of peripheral neuropathy were assigned to Group I. \nGroup II comprised of 30 diabetic patients with no clinical signs and symptoms of \nperipheral neuropathy. 70 healthy volunteers were also recruited for the study, \nand assigned to Group III. The cross sectional area and maximum thickness of \nnerve fascicles of the ulnar nerve were measured at every predetermined site. \nRESULTS \nThe cross sectional area of the ulnar nerve was measured at three sites (inlet of \nthe cubital tunnel, outlet of the cubital tunnel and Guyon tunnel). The mean cross \nsectional area and maximum thickness of nerve fascicles of the ulnar nerves in the \nabove three sites in Group I compared with both Group II and III was significantly \nlarger, and statistically significant correlation was found with the Toronto Clinical \nNeuropathy Score (p<0.001). The Group II patients also had a significantly larger \nmean cross sectional area and maximum thickness of nerve fascicles than Group \nIII. \nCONCLUSIONS \nHigh resolution ultrasonography of ulnar nerve is an easy non-invasive tool for the \nearly diagnosis of diabetic peripheral neuropathy by assessing the cross sectional \narea and maximum thickness of nerve fascicles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".