Conduction Slowing in Diabetic Sensorimotor Polyneuropathy
Bibliographic record
Abstract
OBJECTIVE: Mild demyelination may contribute more to the pathophysiology of nerve fiber injury in diabetic sensorimotor polyneuropathy (DSP) than previously thought. We investigated the clinical and electrodiagnostic classifications of nerve injury in diabetic patients to detect evidence of conduction slowing in DSP. RESEARCH DESIGN AND METHODS: Type 1 diabetic subjects (n = 62) and type 2 diabetic subjects (n = 111) with a broad spectrum of DSP underwent clinical examination and nerve conduction studies (NCS). Patients were classified as having axonal (group A), conduction slowing (group D), or combined (group C) DSP based on electrodiagnostic criteria. Patients with chronic immune-mediated neuropathies were not included. The groups were compared using ANOVA, contingency tables, and Kruskal-Wallis analyses. RESULTS: Of the 173 type 1 and type 2 diabetic subjects with a mean age of 59.1 ± 13.6 years and hemoglobin A1c (HbA1c) of 8.0 ± 1.8% (64 ± 19.7 mmol/mol), 46% were in group A, 32% were in group D, and 22% were in group C. The severity of DSP increased across groups A, D, and C, respectively, based on clinical and NCS parameters. The mean HbA1c for group D subjects (8.9 ± 2.3% [74 ± 25.1 mmol/mol]) was higher than for group A and group C subjects (7.7 ± 1.4% [61 ± 15.3 mmol/mol] and 7.5 ± 1.3% [58 ± 14.2 mmol/mol]; P = 0.003), and this difference was observed in those with type 1 diabetes. CONCLUSIONS: The presence of conduction slowing in patients with suboptimally controlled type 1 diabetes indicates the possibility that this stage of DSP may be amenable to intervention via improved glycemic control.
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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 | 0.001 |
| 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 | 0.000 | 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".