Role of high resolution ultrasound as a screening tool in peripheral neuropathy
Bibliographic record
Abstract
Objective: Early detection of nerve dysfunction is important in management of diabetic peripheral neuropathy.Our study was aimed at finding the correlation of cross sectional area and Maximum thickness of nerve fascicle with the presence of peripheral neuropathy.Materials and Methods: 50 patients with type 2 diabetes clinically diagnosed with diabetic peripheral neuropathy were analysed and severity of peripheral neuropathy was determined using Toronto clinical neuropathic score.45 diabetic patients with no symptoms of peripheral neuropathy and 50 healthy nondiabetic subjects were taken as controls.The cross sectional area and maximum thickness of nerve fascicles of the tibial nerve were calculated 3cm cranial to medial malleolus in both lower limbs.Results: The mean cross sectional area (17.01+/-1.31mm 2 ) and maximum thickness of nerve fascicle (0.61 mm) of the tibial nerve in patients with peripheral neuropathy compared with both control groups were significantly larger and statistically significant correlation was found with Toronto clinical neuropathic score(p<0.001).the diabetic patients with no signs of peripheral neuropathy had larger mean cross sectional area(10.77+/-1.73mm 2 ) and maximum thickness of nerve fascicle (0.25mm) than the healthy non-diabetic subjects (7.46+/-1.77mm 2 and 0.20 mm respectively).Conclusion: The cross sectional area and mean thickness of nerve fascicle of the tibial nerve is larger in diabetic patients with or without peripheral neuropathy than in healthy subjects and it showed correlation with severity and high resolution USG can be used as a screening tool in these patients.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".