The functional movement test 9+ is a poor screening test for lower extremity injuries in professional male football players: a 2-year prospective cohort study
Bibliographic record
Abstract
BACKGROUND: The 9+ screening battery test consists of 11 tests to assess limitations in functional movement. AIM: To examine the association of the 9+ with lower extremity injuries and to identify a cut-off point to predict injury risk. METHODS: Professional male football players in Qatar from 14 teams completed the 9+ at the beginning of the 2013/2014 and 2014/2015 seasons. Time-loss injuries and exposure in training and matches were registered prospectively by club medical staff during these seasons. Univariate and multivariate Cox regression analyses were used to calculate HR and 95% CI. Receiver operating characteristic (ROC) curves were calculated to determine sensitivity and specificity and identify the optimal cut-off point for risk assessment. RESULTS: 362 players completed the 9+ and had injury and exposure registration. There were 526 injuries among 203 players (56.1%) during the two seasons; injuries to the thigh were the most frequent. There was no association between 9+ total score and the risk of lower extremity injuries (HR 1.02, 95% CI 0.99 to 1.05, p=0.13), even after adjusting for other risk factors in a multivariate analysis (HR 1.01, 95% CI 0.98 to 1.04, p=0.37). ROC curve analysis revealed an area under the curve of 0.48, and there was no cut-off point that distinguished injured from non-injured players. CONCLUSION: The 9+ was not associated with lower extremity injury, and it was no better than chance for distinguishing between injured and uninjured players. Therefore, the 9+ test cannot be recommended as an injury prediction tool in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".