Motor unit potential characterization using “pattern discovery”
Bibliographic record
Abstract
Typically in clinical practice, electromyographers use qualitative auditory and visual analysis of electromyographic (EMG) signals to help infer if a neuromuscular disorder is present and if it is neuropathic or myopathic. Quantitative EMG methods exist that can more accurately measure feature values but require qualitative interpretation of a large number of statistics. Electrophysiological characterization of a neuromuscular system can be improved through the quantitative interpretation of EMG statistics. The aim of the present study was to compare the accuracy of pattern discovery (PD) characterization of motor unit potentials (MUPs) to other classifiers commonly used in the medical field. In addition, a demonstration of PD's transparency is provided. The transparency of PD characterization is a result of observing statistically significant events known as patterns. Using clinical MUP data from normal subjects and patients with known neuropathic disorders, PD achieved an error rate of 30.3% versus 29.8% for a Naïve Bayes classifier, 30.1% for a Decision Tree and 29% for discriminant analysis. Similar results were found for simulated EMG data. PD characterization succeeded in interpreting the information extracted from MUPs and transforming it into knowledge that is consistent with the literature and that can be valuable for the capture and transparent expression of clinically useful knowledge.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".