Average proportional consecutive interval difference accurately differentiates spontaneous activity from motor unit potentials
Bibliographic record
Abstract
INTRODUCTION: An objective method is required to detect spontaneous activity (SA) for prevalence studies in needle electromyography (EMG). Because of frequent similarities in the morphology of SA and motor unit potentials (MUP), identification of SA depends on assessment of firing regularity, which has not yet been quantitated through a modern interface. METHODS: Prospective recordings obtained from patients referred for electrodiagnostic evaluation were analyzed by using decomposition-based quantitative EMG (DQEMG) customized to calculate descriptive statistics. RESULTS: Forty-four MUP recordings (39 participants) and 80 SA recordings (62 participants) were analyzed. One hundred one of 124 recordings successfully interfaced with DQEMG. The remaining recordings were analyzed in Audacity. Average proportional consecutive interval differences differentiated SA from MUPs with 97.5% sensitivity (confidence interval [CI] 91.3%-99.7%) and 100.0% specificity (CI 92%-100%). There was substantial overlap, however, for SD and mean consecutive differences. DISCUSSION: Average proportional consecutive interval difference accurately differentiates SA from MUPs and may be useful in future prevalence studies of SA.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".