Little associations exist between the three commonly used functional screening tests in collegiate athletes
Bibliographic record
Abstract
Although an abundant number of studies have investigated the predictability of the commonly used functional screening tests and despite their popularity and applicability, the relationships between these tests have rarely been studied and have not been well established. This study aimed to examine the potential association between the Functional Movement Screen (FMS), Y Balance Test (YBT), and Landing Error Scoring System (LESS). Six hundred twenty-seven Iranian collegiate athletes (347 males, age = 22.63 ± 4.07, weight = 75.98 ± 13.79, height = 181.99 ± 10.15, BMI = 22.84 ± 3.16; and 280 females, age = 22.22 ± 3.37, weight = 60.63 ± 9.58, height = 166.55 ± 6.49, BMI = 21.81 ± 2.84) participated in this study. Following a 5-min warm-up, each participant underwent a standardized screening battery including the FMS, YBT, and LESS, and the scores were recorded and live coded for the statistical analysis, except for the LESS. The LESS tests were video recorded and scored by one expert examiner using an open-source 2D video analysis software (Kinovea- version 0.9.5), afterwards. The Spearman correlation was utilized as a measure for the correlation, and the Mann‒Whitney U test with a significance level of 0.05 was used to check the differences between male and female athletes. The statistical analysis was performed with RStudio 2023.03.0 using R 4.3.1. A small correlation (0.364) was observed between the FMS composite score and the YBT in male athletes. All other pairwise correlations were negligible among male and female athletes, ranging from - 0.096 to 0.294. Reducing the FMS to the component scores targeting the lower extremities did not alter the correlation to the other screening scores. The median FMS composite score in female athletes was significantly higher than that in males (p < 0.001). Negligible correlations exist between the FMS, LESS, and YBT; they do not measure the same values and therefore are irreplaceable with one another. A combination of these tests as a standardized screening battery may potentially better identify injury-predisposed athletes than the application of each test as a stand-alone screening test. Females outperformed males in the FMS test significantly, so sex must be considered a key variable in the FMS studies. Males had slightly higher LESS scores (median difference = 0.5) than females, but this difference is not clinically meaningful. Future research should continue to explore the relationships between various functional screening tests and identify the most effective combinations for comprehensive assessment in different populations and sports disciplines.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".