Effect of trunk rotation and arm position on gross upper extremity adduction strength and muscular activity
Bibliographic record
Abstract
The aim of the experiment was to determine the impact of axial trunk rotation and arm position on upper extremity adduction force and muscle activity. Ten healthy male subjects performed graded maximum voluntary contractions under isometric conditions in seven upper extremity positions and three trunk postures (neutral and 90 degrees left/right rotated) in a simulated manual materials handling task. A custom built lightweight force-measuring device was held between the palmar surfaces of the hands and subjects compressed the lateral surfaces of the device. Muscle activity was recorded bilaterally over the muscle bellies of the anterior deltoid, the long head of the biceps brachii and over the flexor carpi radialis. The activity of the right pectoralis major was also recorded unilaterally. Descriptive, multivariate analysis of variance (MANOVA) and post-hoc Scheffé comparisons were performed on the mean and peak force as well as the EMG [electromyographic] data. Further analysis was performed on the force-EMG relationship at 20% intervals of maximum voluntary contraction (force). Both upper extremity adduction force and EMG were significantly affected by position (p<0.01) but not by trunk rotation. The muscle activity increased and force decreased with flexion of the upper extremity. Pearson correlation coefficients between force and EMG were low. The biceps and flexors were the most active muscles depending upon upper extremity position, and the right pectoralis major muscle activity expressed the highest correlation with force. The present findings confirm earlier hypotheses that upper extremity adduction strength is not significantly affected by trunk rotation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.005 | 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".