Effect of Core Stability Trainings on Functional Movement Screening Scores and Trunk Muscle Endurance in Female Kyokushin Karate Athletes
Bibliographic record
Abstract
Background: The objective of this study was to examine the impact of six weeks of core instability training on functional movement screening (FMS) scores and core trunk endurance in female Kyokushin karate athletes. Methods: Thirty female Kyokushin Karate athletes aged 14 - 18 years were recruited and randomly assigned to either an experimental group or a control group (n = 15 per group). Functional movement patterns, including the FMS, Sorenson, and McGill tests, were assessed before and after the intervention. The experimental group underwent six weeks of Kyokushin Karate training, while the control group performed core stability exercises. The statistical analysis involved paired t-tests and analysis of covariance (ANCOVA), with a significance level set at P ≤ 0.05. Results: The paired t-test results indicated a significant difference in pre- and post-test scores in both the control and experimental groups (P < 0.05). However, the ANCOVA showed no significant differences between the groups (P > 0.05). Conclusions: Both Kyokushin training and core stability exercises have been found to increase core stability and FMS scores. Therefore, it can be suggested that Kyokushin Karate athletes may not need to perform separate core stability exercises as part of their training routine.
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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) | 0.002 | 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".