Using axon models to interpret electrodiagnostic nerve tests
Bibliographic record
Abstract
Automated nerve excitability testing is a relatively new electrodiagnostic technique that became commercially available in 2007 [ 1 ]. The purpose of an excitability test is to infer the underlying membrane properties of the nerve in order to detect ion channel disorders in vivo . This is accomplished by using both supra- and sub-maximal conditioning stimuli of different amplitudes and latencies with respect to a test stimulus. The standard clinical protocol for motor axons includes four tests: 1) strength-duration; 2) recovery cycle; 3) threshold electrotonus; and 4) current-threshold. The interpretation of the four tests is complicated and mathematical models have been essential for explaining unexpected results [ 2 ]. This study's objectives were to: 1) compare two candidate motor axon models for interpreting nerve excitability studies; 2) perform a sensitivity analysis to establish correlations between membrane biophysics and clinical outcome measures; and 3) develop an optimization routine for fitting the models to experimental data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".