Evolution of muscular fatigue in periscapular and rotator cuff muscles during isokinetic shoulder rotations
Bibliographic record
Abstract
To this day, how shoulder muscles react to a strong fatigue stimulus during dynamic shoulder rotations remains unknown. The aims of this study were to assess the effect of repeated maximal internal-external isokinetic shoulder rotations on shoulder strength and muscle activity. Twenty-four individuals completed a 50-repetition fatiguing isokinetic protocol while electromyography was recorded on eleven muscles of the shoulder girdle. Time-frequency transformation and an ANOVA model using statistical parametric mapping methods were used to analyze shifts in instantaneous median frequency (MDF) between each 10-repetition Blocks. Peak torques decreased in both internal and external rotation (P < 0.01) by 24.8% on average which indicated the presence of fatigue. Significant decrease in MDF (P < 0.01) was observed for pectoralis, middle deltoid, upper, middle and lower trapezius, infraspinatus and subscapularis muscles. The observed fatigue to the periscapular and rotator cuff muscles suggests that shoulder stability could be compromised during repeated shoulder rotations, which could underlie the increased risk of scapular dyskinesis and shoulder impingement during fatiguing tasks. The present study provides a deeper understanding on the manifestations of fatigue within muscles of the shoulder girdle and the results could be applied toward improvements in athlete shoulder injury prevention and rehabilitation programs.
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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 | 0.001 |
| 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".