Supramaximal Stimulus Intensity as a Diagnostic Tool in Chronic Demyelinating Neuropathy
Bibliographic record
Abstract
Objective. The ability to correctly identify chronic demyelinating neuropathy can have important therapeutic and prognostic significance. The stimulus intensity value required to obtain a supramaximal compound muscle action potential amplitude is a commonly acquired data point that has not been formally assessed as a diagnostic tool in routine nerve conduction studies to identify chronic neuropathies. We postulated that this value was significantly elevated in chronic demyelinating neuropathy. Methods. We retrospectively reviewed electrophysiology laboratory records to compare the stimulus intensity values recorded during median and ulnar motor nerve conduction studies. The groups studied included normal controls (n = 42) and the following diagnostic categories: chronic inflammatory demyelinating neuropathy (CIDP) (n = 20), acquired inflammatory demyelinating neuropathy (AIDP) (n = 13), Charcot Marie Tooth (CMT) type 1 or 4C (n = 15), carpal tunnel syndrome (CTS) (n = 11), and amyotrophic lateral sclerosis (ALS) (n = 18). Results. Supramaximal intensities were significantly higher in patients with CMT (median nerve: 43.4 mA) and CIDP (median nerve: 38.9 mA), whereas values similar to normal controls (median nerve: 25.3 mA) were obtained in ALS, CTS, and AIDP. Conclusions. Supramaximal stimulus intensity may be used as an additional criterion to identify the pathophysiology of neuropathy. We postulate that endoneurial hypertrophic changes may increase electrical impedance and thus the threshold of excitation at nodes of Ranvier.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".