The relationship between pectoralis minor length, scapular muscle endurance and core endurance in athletes
Bibliographic record
Abstract
Objective: The purpose of this study was to identify relationships between core endurance, scapular muscle endurance and pectoralis minor length in athletes. Methods: 69 professional athletes in different branches (44 men, 25 women, mean age 20.41±5.72, height 176.0±0.1, and weight 68.2±12.8) were included to this study. Pectoralis minor length was measured in supine. In this position linear distance from treatment table to posterior aspect of the acromion was measured. Core endurance was evaluated using Mcgill Core Endurance Tests. Scapular muscle endurance was assessed with scapular endurance test. Statistical analysis was performed using the statistical software SPSS. Results: Spearman correlation analysis results showed that there was correlation between pectoralis minor length and scapular muscle endurance (r=0.281, p<0.05). A positive correlation was found between pectoralis minor length and core endurance (r= 0.517, p<0.05). There was correlation between scapular muscle endurance and core endurance (r=0.524, p<0.05). Conclusions: In this study, it was shown that there were statistically significant relationship between pectoralis minor length, scapular muscle endurance and core endurance. Pectoralis minor length affects scapula biomechanics. Change in scapula biomechanics may cause reduction in scapular muscle endurance. It may affect core endurance via kinetic chain. Therefore, it can be suggested pectoralis minor stretching should be integrated to normal sports specific training programmes in professional 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) | 0.003 | 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".