The relationship between postural stability, core muscles endurance and agility in professional basketball players
Bibliographic record
Abstract
Aim: The purpose of this study was to identify relationships between postural stability, core muscle endurance and agility in basketball players. Materials and Methods: 21 professional male basketball players [age: 17.±0.63 (years), body weight: 78.69±9.22 (kg), height: 186±7 (cm), BMI: 22.72±2.26 (kg/cm²)] were included to this study. Three balance variables were measured using Biodex Biosway™. Core muscle endurance was measured using three core endurance tests proposed by Mcgill. Agility was assessed by Hexagonal Obstacle Test (HOT). Results: The analysis results showed that postural stability is associated with HOT (r: 0.457). In addition, the postural stability was related to extension muscle endurance test and side bridge test (respectively, r: -0.501 and r: -0.468). There is no correlation between HOT and core muscle endurance test in basketball players. Conclusion: The athletes with good postural stability were better core muscle endurance and agility. It is important to apply exercise programs that enhance postural stability and increase core muscle endurance to improve sportive success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".