Pilot study: quantification of motor unit stability, or “jiggle”, in amyotrophic lateral sclerosis (731.11)
Bibliographic record
Abstract
Loss of motor units (MUs) have been found in aging, neuropathies, and motor neuron diseases. With MU loss, subsequent collateral reinnervation results in unstable motor unit potentials (MUPs) with variability in firing times and generation of muscle fiber action potentials. Jiggle is a measure of MUP stability, which can be calculated using Decomposition‐based Quantitative Electromyography (DQEMG). Two previous studies have attempted to measure jiggle, but both studies were limited by the EMG decomposition algorithms used. The purpose of this study is to measure jiggle in the upper trapezius muscle of 11 amyotrophic lateral sclerosis (ALS) patients compared to age‐matched healthy controls. Jiggle will be calculated using the normalized value of the median consecutive amplitude difference (CAD). Jiggle will also be compared to other MUP parameters including motor unit number estimation (MUNE), mean surface MUP (S‐MUP) size, MUP amplitude, and near fiber count. As a result of denervation and incomplete reinnervation, we expect to see significantly higher jiggle values in patients with ALS relative to controls. We also expect jiggle to be negatively correlated with MUNE, and positively correlated with mean S‐MUP size, MUP amplitude, and near fiber count. If successful, jiggle may be a valuable diagnostic parameter or outcome measure in age‐related sarcopenia, neuropathies, or motor neuron diseases such as ALS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".