L’analyse des facteurs de risque et de leur évaluation est-elle efficace pour prédire les risques d’entorse de cheville chez les footballeurs
Bibliographic record
Abstract
Introduction : Ankle sprains are one of the most common sport injuries with a very high recurrence rate. This type of injuries is particurlarly predominant in soccer which can be due to the fact that it’s risks factors are not assessed during pre-season screening. Goal : The goal of this study is to investigate which factors are the most reliable to predict the risk of future sprains. Method : To try and answer this question, multiple databases were searched (PEDro, Pubmed, Cochrane, Google Scholar and ScienceDirect.) from October 2024 to January 2025. After the first selection 505 articles were initially included and were later reduced to 6 after a thorough analysis of their abstract and a integral lecture of each study. The intrinsic quality of these articles was analysed using the Newcastle-Ottawa Scale given that the question is an etiologic one. Results : The final 6 studies consisted of 5 prospectives cohorts and 1 case-control study. The results concerning the reliability and predictive value of the investigated risks factors are heterogenous. Nevertheless some factors, hip abduction strength, inferior limb power output, balance, eccentric ankle strength asymetries, ground reaction forces and BMI/Weight were found to be useful for predicting sprains. Discussion : This study identifies hip abduction strength, inferior limb power output, balance,eccentric ankle strength asymetries, ground reaction forces and BMI/Weight as predictive risk factors for ankle sprains. However the heterogeneity and the low level of evidence of this review implies that further research needs to be done in order know which factors are worth examining in everyday soccer screening.
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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 | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".