Near-fiber electromyography
Bibliographic record
Abstract
OBJECTIVE: Describe and evaluate the concepts of near fiber electromyography (NFEMG), the features used, including near fiber motor unit potential (NFMUP) duration and dispersion, which relate to motor unit distal axonal branch and muscle fiber conduction time dispersion, and NFMUP segment jitter, a new measure of the temporal variability of neuromuscular junction transmission (NMJ), and axonal branch and muscle fibre conduction for the near fibres (i.e. NF jitter), and the methods for obtaining their values. METHODS: Trains of high-pass filtered motor unit potentials (MUPs) (i.e. NFMUP trains) were extracted from needle-detected EMG signals to assess changes in motor unit (MU) morphology and electrophysiology caused by neuromuscular disorders or ageing. Evaluations using simulated needle-detected EMG data were completed and example human data are presented. RESULTS: NFEMG feature values can be used to detect axonal sprouting, conduction slowing and NMJ transmission delay as well as changes in MU fiber diameter variability, and NF jitter. These changes can be detected prior to alterations of MU size or numbers. CONCLUSIONS: The evaluations clearly demonstrate and the example data support that NFMUP duration and dispersion reflect MU distal axonal branching, conduction slowing and NMJ transmission delay and/or MU fiber diameter variability and that NFMUP jiggle and segment jitter reflect NF jitter. SIGNIFICANCE: NFEMG can detect early changes in MU morphology and/or electrophysiology and has the potential to augment clinical diagnosis and tracking of neuromuscular disorders.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".