Timing of Lower Extremity Injuries in Competition and Practice in High School Sports
Bibliographic record
Abstract
BACKGROUND: Laboratory-based experiments demonstrate that fatigue may contribute to lower extremity injury (LEI). Few studies have examined the timing of LEIs during competition and practice, specifically in high school athletes across multiple sports, to consider the possible relationship between fatigue and LEIs during sport events. HYPOTHESIS: The purpose of this study was to describe the timing of LEIs in high school athletes within games and practices across multiple sports, with a hypothesis that more and severe injuries occur later in games and practices. STUDY DESIGN: Descriptive epidemiologic study. LEVEL OF EVIDENCE: Level 4. METHODS: Using the National High School RIO (Reporting Information Online) sport injury surveillance system, LEI severity and time of occurrence data during practice and competition were extracted for 9 high school sports. RESULTS: During the school years 2005-2006 through 2013-2014, 16,967,702 athlete exposures and 19,676 total LEIs were examined. In all sports surveyed, there was a higher LEI rate, relative risk for LEI, and LEI requiring surgery during competition than practice. During practice, the majority of LEIs occurred over an hour into practice in all sports. In quarter-based competition, more LEIs occurred in the second (31% to 32%) and third quarters (30% to 35%) than in the first (11% to 15%) and fourth quarters (22% to 26%). In games with halves, the majority (53% to 66%) of LEIs occurred in the second half. The greater severity LEIs tended to occur earlier in games. CONCLUSION: Fatigue may play a role in the predominance of injuries in the second half of games, though various factors may be involved. Greater severity of injuries earlier in games may be because of higher energy injuries when athletes are not fatigued. CLINICAL RELEVANCE: These findings can help prepare sports medicine personnel and guide further related research to prevent LEIs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".