External abdominal oblique muscle ultrasonographic thickness changes is not an appropriate surrogate measure of electromyographic activity during isometric trunk contractions
Bibliographic record
Abstract
BACKGROUND: The function of specific abdominal muscles can be assessed using both electromyography (EMG) and ultrasound imaging (USI) thickness measures. However, the relationship between these two measurements is not conclusive during sitting isometric trunk efforts. OBJECTIVE: This study was conducted to assess the relationship between USI thickness and EMG amplitude measures of the right external oblique (EO) muscle during isometric efforts in the sitting position. METHOD: Eighteen subjects performed ramp isometric efforts progressing from 0 to 50% of their maximal voluntary contraction (MVC) in three trunk directions on a dynamometer: (1) forward flexion; (2) right lateral flexion; and (3) left axial rotation. USI and surface EMG amplitude measures of the EO muscle were recorded concomitantly and both normalized against rest values and maximal EMG, respectively. RESULTS: EO muscle was significantly more activated (p < 0.001) during forward flexion (42% on average) and axial rotation (35%) than during lateral flexion (24%). Non-significant (r=0.01; P=0.979) to highly significant (r=0.98; P < 0.0001) and negative and positive Pearson correlations were observed between EMG and EO thickness measures for both flexion and rotation directions. CONCLUSION: The negative correlations between EMG and USI measures as well as the great variability of these correlations across individuals suggest that USI is not a valid measures of EO muscle activity. USI thickness measures should be interpreted with great caution in research and clinical settings.
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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 | 0.000 | 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".