Activation of Supraspinatus and Infraspinatus Partitions and Periscapular Musculature During Rehabilitative Elastic Resistance Exercises
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to quantify the activation of partitions within supraspinatus and infraspinatus and some periscapular muscles during four resistance exercises with elastic bands. DESIGN: Twenty-seven right-handed healthy volunteers (age, 22.5 ± 2.7 yrs) were recruited. Intramuscular electromyography from supraspinatus (anterior and posterior) and infraspinatus (superior and middle) and surface electromyography data from the upper, middle, and lower trapezius and serratus anterior were recorded during four elastic resistance exercises (Y, T, W, L). Kinematics were recorded synchronously. Electromyography values were presented as percentage of maximal voluntary isometric contraction and compared across exercises using analysis of variance. Muscle activation ratios were also calculated. RESULTS: The mean activations of all rotator cuff partitions were more than 40% maximal voluntary isometric contraction, except middle infraspinatus during the T exercise (29.3% maximal voluntary isometric contraction). Serratus anterior activity was significantly higher during the Y exercise (P < 0.008). Lower trapezius was activated more than 80% maximal voluntary isometric contraction in all four exercises with higher contributions compared with the upper trapezius. CONCLUSIONS: The investigated exercises induced moderate to high activation in supraspinatus and infraspinatus partitions and very high activation in lower trapezius. YTWL exercises are appropriate for strengthening of some rotator cuff and periscapular muscles and for late stages of shoulder rehabilitation.
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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) | 0.002 | 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".