Evaluation scale and definitions of core and core stability in sports: A systematic review
Bibliographic record
Abstract
BACKGROUND: Core stability has been reported to be important for improving performance in athletes. However, the variety of measures used to assess core stability has made it difficult to compare results across studies. In addition, there is a lack of consensus on precise definitions of core and core stability, which is a barrier to research in this field. OBJECTIVE: The overall purpose of this review was to summarize the definitions of core and core stability and measurements of core stability used in previous studies on athletes. METHODS: We searched four electronic databases (PubMed/Medline, SPORTDiscus, Web of Science and Science Direct) from their inception to October 2023. Studies evaluating core stability in athletes across all sports were included. We excluded case studies and case series, opinion pieces, letters to editors and studies not written in the English language. Two researchers independently assessed articles for inclusion and exclusion criteria and methodological quality. RESULTS: One hundred thirty-four studies were included, of which two were of high quality. The definitions of core and core stability varied widely, and ‘core’ was not defined in 108 studies and ‘core stability’ was not defined in 105 studies. The most used test protocol was the McGill test, which was used in 19 studies. CONCLUSIONS: There are multiple tests to measure core stability, and there is some confusion as to whether the measurement results represent core strength or core endurance. Future research papers should clarify the definitions of core and core stability, and consider core strength and core endurance separately.
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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 | 0.045 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".