Predictive models of injury risk in male professional football players: a systematic review
Bibliographic record
Abstract
BACKGROUND: One of the challenges for professional football players is injuries. Due to their influence on their teams, injuries greatly impact the sports business. This research aims to assess predictive models of injury risk in male professional football players. METHODS: A systematic literature review was performed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was conducted in the PubMed, Web of Science and Scopus databases. Two independent reviewers screened articles, assessed eligibility and extracted data. Methodological quality was determined by the Newcastle-Ottawa Scale. RESULTS: 26 studies met the inclusion criteria. DISCUSSION: Various statistical techniques were used in research on injury prediction in professional football, with logistic regression being the most used. The assessment predictors, especially the area under the receiver operating characteristic Curve, showed significant variation, which indicates the prediction models' efficacy. The focus was frequently on lower limb injuries, where several risk predictors, including muscular strength, flexibility and global positioning system-derived data, were found to substantially impact the occurrence of injuries. Prominent predictors included age, position, physiological parameters, injury history and genetic polymorphisms. CONCLUSIONS: This comprehensive analysis highlights the complexity of injury prediction and reinforces the necessity for football injury research to adopt a multivariate approach with accuracy and comprehensiveness. PROSPERO REGISTRATION NUMBER: CRD42023465524.
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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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".