Probabilistic muscle characterization using quantitative electromyography: Application to facioscapulohumeral muscular dystrophy
Bibliographic record
Abstract
Based on quantitative electromyography, a muscle can be categorized as normal or affected by a neuromuscular disorder. The objective of this work was to compare the utility of probabilistic to conventional means and outlier methods of categorization of myopathic and normal muscles. Various sets of motor unit potential (MUP) features detected in biceps brachii muscles of control subjects and patients with facioscapulohumeral muscular dystrophy were used to categorize them as normal or myopathic based on conventional means and outlier categorization (CMC) as well as a new probabilistic muscle categorization (PMC). The sensitivity, specificity, and accuracy provided by each categorization method were compared. The categorizations made using PMC were significantly more accurate (by at least 10%) compared with CMC (P < 10(-10)) for muscles evaluated in this study. Area, duration, and thickness were highly discriminative MUP features.
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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 | 0.001 | 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 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".