Lower Trapezius Weakness and Shoulder Complex Biomechanics during the Tennis Serve
Bibliographic record
Abstract
PURPOSE: This study aimed to assess the effect of lower trapezius (LT) weakness on humeral and scapular kinematics and shoulder muscle activity during the tennis serve. METHODS: Fifteen competitive male tennis players (age, 23.8 ± 3.4 yr; height, 182.8 ± 6.7 cm; mass: 76.6 ± 8.7 kg; tennis experience: 15.6 ± 4.9 yr) performed two tennis serves before and after selective fatigue of the LT (25-min electric muscle stimulation). During each tennis serve, racket, humeral and scapular kinematics and the activity of 13 shoulder muscles were recorded using an optoelectronic system synchronized with indwelling and surface electromyography. The serve was split into five phases, that is, early and late cocking, acceleration, early and late follow-through. RESULTS: Selective fatigue led to a 22.5% ± 10.4% strength decrease but did not alter maximum racket speed and humerothoracic joint kinematics. However, increased scapular upward rotation was observed in the acceleration (P = 0.02) and early follow-through (P = 0.01) phases. Decreased muscular activity was observed during the early cocking phase for the LT (P = 0.01), during the acceleration phase for the LT (P = 0.01), anterior deltoid (P = 0.03), pectoralis major (P = 0.04), and subscapularis (P = 0.03), and during the early follow-through phase for the anterior deltoid (P = 0.03) and LT (P = 0.04). CONCLUSIONS: The LT weakness altered neither serve velocity nor humerothoracic joint kinematics, but impaired scapulothoracic kinematics and anterior shoulder muscle activation. Such alterations may reduce the subacromial space and jeopardize humeral head stability. These findings shed new light on the consequences of LT weakness, highlighting the importance of monitoring and strengthening this muscle in overhead athletes.
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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".