Early detection of peripheral neuropathy in patients with diabetes mellitus type 2
Bibliographic record
Abstract
Abstract Background Early diagnosis of diabetic polyneuropathy (DPN) can significantly improve the prognosis and help prevent severe complications. The aim of this work was to study clinical, radiological, laboratory and neurophysiological findings for early detection of peripheral neuropathy in T2DM. Methods A total of 60 diabetic patients were classified according to Toronto Clinical Neuropathy Score (TCNS) into: Group 1: 20 diabetic patients with no evident neuropathy. Group 2: 20 diabetic patients with mild neuropathy. Group 3: 20 diabetic patients with moderate and severe neuropathy. All patients underwent a neurological examination, nerve conduction studies and optical coherence tomography (OCT) to assess retinal nerve fiber layer (RNFL) thickness. Additionally, ELISA technique to measure serum interleukin-6 (IL-6). Results The analysis of gender and age distributions among the groups revealed no significant differences. There were statistically significant differences regarding disease duration, HBA1c, body mass index Systolic and diastolic blood pressure. Group 3 had such significant impairment that resulted in an inability to record the measurements of sural nerves. The study's statistical analysis results for OCT variables, and post hoc comparisons revealed significant differences between all three groups. The results demonstrated significant variations in Serum IL6 levels among the groups, with Group 3 having the highest IL6 levels. In groups 1, 2, and 3 the area under the curve for IL-6 and RNFL showed a good differentiation ability between groups. Conclusion We conclude that the total thickness RNFL and serum IL-6 levels are a potential biomarker in prediction the severity of DPN.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".