Glycemic Control Is Related to the Morphological Severity of Diabetic Sensorimotor Polyneuropathy
Bibliographic record
Abstract
OBJECTIVE: The aim of the current study was to determine the independent clinical risk factors for predicting morphological severity of distal diabetic sensorimotor polynecuropathy (DSP) as determined by fiber density (FD) on sural nerve biopsy. RESEARCH DESIGN AND METHODS: A total of 89 patients with both type 1 and type 2 diabetes, ascertained from a large therapeutic randomized clinical trial, were included in this observational cohort study. Morphological severity of DSP was expressed as the myelinated FD in the sural nerve biopsy General linear models were used to assess the relationship between the morphological severity of DSP and various clinical risk factors. RESULTS: Glycated hemoglobin (GHb) was significantly related to FD in univariate and multivariate regression analyses. This relationship was present in models in which GHb was handled either as a continuous variable or as a categorical variable with the highest significance level, with a GHb cutoff level of 9%. After dividing patients into groups with optimal to moderate GHb < or = 9%) and suboptimal (GHb >9%) glycemic control, the difference in FD between the two groups ranged between 3,461 and 2,334 per mm2. FD was also significantly related to duration of diabetes and age of the patient. CONCLUSIONS: The severity of peripheral DSP expressed by morphological criteria was significantly related to glycemic control in type 1 and type 2 diabetic patients. Inconsistent with previously published electrophysiological data demonstrating a correlation between height and conduction velocity, increasing height is not associated with morphological severity. Based on the results of the present study, it might be hypothesized that improving glycemic control will lessen severity of DSP in terms of FD loss in subjects with diabetes.
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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 | 0.000 | 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) | 0.002 | 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".