Test-Retest Reliability of Trunk Motor Variability Measured By Large-Array Surface Electromyography
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate the test-retest reliability of the trunk muscle activity distribution in asymptomatic participants during muscle fatigue using large-array surface electromyography (EMG). METHODS: Trunk muscle activity distribution was evaluated twice, with 3 to 4 days between them, in 27 asymptomatic volunteers using large-array surface EMG. Motor variability, assessed with 2 different variables (the centroid coordinates of the root mean square map and the dispersion variable), was evaluated during a low back muscle fatigue task. Test-retest reliability of muscle activity distribution was obtained using Pearson correlation coefficients. RESULTS: A shift in the distribution of EMG amplitude toward the lateral-caudal region of the lumbar erector spinae induced by muscle fatigue was observed. Moderate to very strong correlations were found between both sessions in the last 3 phases of the fatigue task for both motor variability variables, whereas weak to moderate correlations were found in the first phases of the fatigue task only for the dispersion variable. CONCLUSION: These findings show that, in asymptomatic participants, patterns of EMG activity are less reliable in initial stages of muscle fatigue, whereas later stages are characterized by highly reliable patterns of EMG activity.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".