P.102 Role of interdigital sensory nerve conduction study as a noninvasive approach for early diagnosis of diabetic peripheral neuropathy
Bibliographic record
Abstract
Background: Diabetes mellitus is a common cause of polyneuropathy. Despite numerous diagnostic tools such as routine electrophysiologic procedures, its early detection is challenging. This study compares a distal electrodiagnostic technique, with conventional approaches to investigate its role in confirming early polyneuropathy. Methods: Thirty-one symptomatic diabetic outpatients and 23 asymptomatic nondiabetic subjects were included in our study. We performed nerve conduction studies on the dorsal sural, medial plantar, and digital branches of the interdigital nerves to toes I, II, and III (as a new antidromic technique). All techniques were applied with the surface stimulator and pick-up electrodes. Results: Only 9 (29%) of patients had impaired routine NCSs. Interestingly, the results of interdigital nerve studies were abnormal in 17 out of 22 patients with normal routine NCSs. Also, 11 and 13 subjects had impaired medial plantar and dorsal sural nerves conduction studies, respectively. According to this method, the prevalence of detectable diabetic neuropathy increased from 46% to 83%. Conclusions: The digital sensory branches can be easily evaluated with the new antidromic SNAP technique for the early diagnosis of diabetic polyneuropathy, especially in presymptomatic and subclinical neuropathies. This method is simple, non-invasive, sensitive, and reproducible. There is no need for needle electrodes or averaging techniques.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".