Association of Peak Height Velocity and Skeletal Maturity to Injury Incidence in Male Elite Adolescent Football (Soccer) Players—A Systematic Review
Bibliographic record
Abstract
Objective: To analyze the impact of peak height velocity (PHV) and skeletal maturity on the injury incidence and injury burden in adolescent football (soccer) players. Methods: PubMed, Scopus, SPORTDiscus, and Web of Science databases were searched in March 2023, for observational studies focusing on PHV or skeletal maturity status and injuries in youth football players. Risk of bias assessment was performed using the Newcastle Ottawa Scale. The evidence of certainty of outcomes was ranked according to GRADE. We did not conduct statistical meta-analysis synthesis of the results due to substantial heterogeneity in the methods and limited reporting of the original studies. Results: A total of 699 abstracts were screened, and 12 studies were included for review. Eight studies analyzed players regarding the PHV status, and four regarding the skeletal maturity. The certainty of evidence for both outcomes was very low. Regarding PHV, the injury incidence and injury burden were highest in the circa-PHV group in five studies, highest in post-PHV in two studies, and lowest in pre-PHV group in all but one study. Regarding skeletal maturity, in two studies, the injury incidence was highest in early mature and in one study it was highest in normal maturers, with no detectable differences between the groups. In one study, the injury burden was highest in the early maturers. Conclusion: Injury incidence and injury burden may be increased during the peak high-velocity growth period in male elite-level adolescent football players. It is unclear whether the level of skeletal maturity affects the injury incidence or injury burden.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".