Evaluation of the lower extremity functional test to predict lower limb injuries in professional male footballers
Bibliographic record
Abstract
The Lower Extremity Functional Test (LEFT) is a reliable and valid test for the measurement of athletic fitness, fatigue resistance, and speed performance. Contradictory results exist regarding the screening value of the LEFT in predicting lower limb injuries. The purpose of this study was to investigate the screening value of the LEFT in predicting lower limb injuries in professional male footballers. One hundred and twenty-one professional male football players participated in the study. LEFT was recorded pre-season and the lower-limb injuries were recorded during a 9-month season. Logistic regression analysis was used to determine the accuracy of the prognosis of LEFT. A total of twenty-five lower limb injuries were recorded. The model explained 53% of the variance in lower limb injury, showing that predictions by LEFT score is reliable, and correctly predicted 89.3% of cases, which is a large improvement. ROC analysis showed significant accuracy of the LEFT score (AUC 0.908, 95% CI 1.126-1.336, p = 0.001, OR = 1.227) in discriminating between injured and uninjured players. The optimum cut-off level of the LEFT score was 90.21 s; Our findings showed that the LEFT score was able to predict lower limb injuries in professional male footballers. The slower an athlete's LEFT scores, the more susceptible they are to future injury risk. Sports medicine specialists, football coaches and managers are suggested to use LEFT as a pre-season screening test to identify and prevent the weakness and functional imbalance of the athletes before the injury occurs by conducting this test.
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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 | 0.001 | 0.006 |
| 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) | 0.001 | 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".