The Effect of McGill Core Stability Training on Movement Patterns, Shooting Accuracy, and Throwing Performance in Male Basketball Players: A Randomized Controlled Trial
Bibliographic record
Abstract
CONTEXT: Core stability training has been recommended as a vital element in improving movement's pattern and athletic performance. The main objective of this study was to investigate the effect of 12-week McGill core stability training on movement patterns, shooting accuracy, and throwing performance. DESIGN: Randomized controlled clinical trial. SETTING: University research laboratory. METHODS: Forty male basketball players were randomly assigned to experimental and control groups. The experimental group completed 12-week McGill core stability training, while the control group completed routine exercise training. Patterns of functional movements was measured through functional movement screen (FMS), shooting accuracy measured by static 3-point shooting (S3P) and dynamic 60-second 3-point shooting test, and throwing performance measured by Functional Throwing Performance Index. RESULTS: Comparison revealed that regardless of received training, after 12 weeks both groups showed significant improvement in all outcome measures. However, experimental group had significantly higher post test scores in FMS (P = .02), S3P (P = .007), and dynamic 60-second 3-point shooting test (P = .01). For Functional Throwing Performance Index, there was no group differences (P = .96). The results of follow-up assessments showed for all measurements including FMS (P = .03), S3P (P = .004), dynamic 60-second 3-point shooting test (P < .001), and Functional Throwing Performance Index (P = .005); experimental group had higher scores than the control group. CONCLUSIONS: According to the results, implementing McGill core stability training in basketball routine training would be advisable since significant improvement can be obtained in the measured parameters.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".