Decomposition‐based quantitative electromyography: Methods and initial normative data in five muscles
Bibliographic record
Abstract
Quantitative electromyographic (EMG) techniques provide clinically useful information to aid in the diagnosis and follow the course or response to treatment of diseases affecting the motor system. The purpose of this study was to describe a decomposition-based quantitative electromyography method (DQEMG) designed to obtain clinically applicable information relating to motor unit potential (MUP) size and configuration, and motor unit (MU) firing characteristics. Additionally, preliminary normative data were obtained from the deltoid, biceps brachii, first dorsal interosseous, vastus medialis, and tibialis anterior muscles of 13 control subjects. DQEMG was capable of efficiently and accurately extracting MUP data from complex interference patterns during mild to moderate contractions. MUP amplitude, surface-detected MUP (S-MUP) amplitude, MUP duration, number of phases, and MU firing frequencies varied significantly across muscles. The mean parameter values for the individual muscles studied were similar to previous reports based on other quantitative methods. The main advantages of this method are the speed of data acquisition and processing, the ability to obtain MUPs from MUs with low and higher recruitment thresholds, and the ability to obtain both S-MUP or macro-MUP data as well as MU firing rate information.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".