A Novel Sensory Nerve Conduction Study Technique to Evaluate the Sural Nerve From the Forefoot to the Knee
Bibliographic record
Abstract
PURPOSE: Length-dependent polyneuropathy is common. Current electrophysiological methods cannot assess sensory nerve function proximal to the distal calf, limiting their utility in the quantification of severity of length-dependent polyneuropathy. METHODS: The authors developed a novel electrophysiological approach for distal to proximal assessment of the sural nerve between the forefoot and the knee and tested it on 63 healthy young, middle-aged, and old adults. RESULTS: It was feasible to elicit sensory nerve action potentials in the forefoot, ankle, and knee segments of the sural nerve in all subjects. Intraobserver (r = 0.87) and interobserver (r = 0.87) reliability were high. Sensory nerve action potential amplitudes were greatest at the ankle, followed by the knee and forefoot. Sensory nerve action potential amplitudes in the forefoot and ankle were significantly smaller in the old age group (>60 years) compared with the young age group (20-39 years) (P < 0.05). In contrast, neither age nor gender had a significant impact on sensory nerve action potential conduction velocities. CONCLUSIONS: The authors demonstrated that reliable electrophysiological recordings of the sural nerve as proximal as the knee are feasible. This novel technique may be useful in patients with length-dependent polyneuropathy to monitor progression and to evaluate treatment response.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".