Motor unit number estimates and quantitative motor unit analysis in healthy subjects and patients with amyotrophic lateral sclerosis
Bibliographic record
Abstract
Limitations associated with global measures of function in patients with amyotrophic lateral sclerosis (ALS) and the qualitative nature of needle electromyography have stimulated the development of alternate means of monitoring disease severity and progression in ALS. Thus, the objective of this study was to examine the ability of one these techniques, decomposition-based quantitative electromyography (DQEMG), to obtain electrophysiological data, including motor unit number estimates (MUNEs), from a group of patients with ALS. The first dorsal interosseous and biceps brachii muscles were studied in 10 healthy subjects and 9 patients with ALS. Following the acquisition of a maximum M wave, needle- and surface-detected EMGs were collected simultaneously during 30-second contractions performed at 10% of the maximum voluntary contraction force to obtain motor unit potential (MUP) trains. DQEMG was then used to extract the surface-detected MUP associated with each MUP train, the mean size of which was divided into the maximum M wave to obtain a MUNE. The results suggest that quantitative electrophysiological data obtained using DQEMG are representative of the pathophysiological changes in the lower motor system in ALS patients, supporting its use in studies documenting the natural history and progression of the disease.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".