A Review of Clinical Quantitative Electromyography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Information regarding the morphology of motor unit potentials (MUPs) and motor unit firing patterns can be used to help diagnose, treat, and manage neuromuscular disorders. In a conventional electromyographic (EMG) examination, a clinician manually assesses the characteristics of needle-detected EMG signals across a number of distinct needle positions and forms an overall impression of the condition of the muscle. Such a subjective assessment is highly dependent on the skills and level of experience of the clinician, and is prone to a high error rate and operator bias. Quantitative methods have been developed to characterize MUP waveforms using statistical and probabilistic techniques that allow for greater objectivity and reproducibility in supporting the diagnostic process. In this review, quantitative EMG (QEMG) techniques ranging from simple reporting of numeric MUP values to interpreted muscle characterizations are presented and reviewed in terms of their clinical potential to improve status quo methods. QEMG techniques are also evaluated in terms of their suitability for use in a clinical decision support system based on previously established criteria. Aspects of prototype clinical decision support systems are then presented to illustrate some of the concepts of QEMG-based decision making.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 it