Comparison of diabetes patients with “demyelinating” diabetic sensorimotor polyneuropathy to those diagnosed with <scp>CIDP</scp>
Bibliographic record
Abstract
BACKGROUND: We have previously identified a subset of diabetic sensorimotor polyneuropathy (DSP) patients with probable demyelination related to poor glycemic control. We aimed to determine whether the clinical characteristics and electrodiagnostic classification of nerve injury in diabetes patients with "demyelinating" DSP (D-DSP) differed from those diagnosed with chronic inflammatory demyelinating polyneuropathy (CIDP) (CIDP + diabetes mellitus [DM]). METHODS: D-DSP (56) and CIDP + DM (67) subjects underwent clinical examination and nerve conduction studies (NCS), and were compared using analysis of variance, contingency tables, and Kruskal-Wallis analyses. RESULTS: Of the 123 subjects with a mean age of 60.5 ± 15.6 years and mean hemoglobin A1c (HbA1c) of 8.2 ± 2.2%, 54% had CIDP + DM and 46% had D-DSP. CIDP + DM subjects were older (P = 0.0003), had shorter duration of diabetes (P = 0.005), and more severe neuropathy as indicated by Toronto Clinical Neuropathy Score (TCNS) (P = 0.003), deep tendon reflexes (P = 0.02), and vibration perception thresholds (VPT) (P = 0.01, P = 0.02). The mean HbA1c value for D-DSP subjects (8.9 ± 2.3%) was higher than in CIDP + DM subjects (7.7 ± 2.0%, P = 0.02). CONCLUSIONS: The clinical phenotype and electrophysiological profile of CIDP + DM patients is marked by more severe neuropathy and better glycemic control than in patients with D-DSP. These findings indicate that these two conditions - despite similarities in their electrophysiological pattern of demyelination - likely differ in etiology.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".