Bibliographic record
Abstract
Purpose: The primary aim of this study was to examine the effects of 8-week lower trapezius strengthening exercise (LTSE) on shoulder pain, function and archery performance. The secondary aim was to identify main factors that have something to do with injury prevention and performance enhancement for elite archers. Methods: Thirty-one elite archers were recruited and evenly assigned into the LTSE group (n=16) and into the control group (n=15) based on gender and athletes’ career. Shoulder pain was evaluated using Numeric pain rating scale (NPRS). Shoulder function was assessed using the Western Ontario Shoulder Instability Index (WOSI), upper quarter Y balance test (UQYBT), Trapezius and Deltoid muscle activity ratios by surface electromyography and the angle of scapula elevation/abduction by 3-dimentional motion analyses. Archery performance was estimated using draw force line (DFL) angle at full bowstring draw position and the scores acquired from real archery shooting. After the baseline measurements, the 8-week LTSEs were implemented and the post-exercise measurements were conducted. Results: In the LTSE group, NPRS score and WOSI score significantly decreased after exercise program. The activity ratio of upper to lower trapezius muscle, scapula elevation angle and the DFL angle were also significantly reduced. The UQYBT scores significantly increased on both shoulders. Conclusion: Eight weeks of LTSE has reduced shoulder pain in archers and improved shoulder function and performance factors.
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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 | 0.000 |
| 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) | 0.003 | 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".