Sports-Related Injuries in Youth Athletes: Is Overscheduling a Risk Factor?
Bibliographic record
Abstract
OBJECTIVE: To examine the association between "overscheduling" and sports-related overuse and acute injuries in young athletes and to identify other potential contributing factors to create a working definition for "overscheduling injury." DESIGN: Survey. SETTING: Six university-based sports medicine clinics in North America. PARTICIPANTS: Athletes aged 6 to 18 years (13.8 ± 2.6) and their parents and pediatric sports medicine-trained physicians. INTERVENTIONS: Questionnaires developed from literature review and expert consensus to investigate overscheduling and sports-related injuries were completed over a 3-month period. MAIN OUTCOME MEASURES: Physician's clinical diagnosis and injury categorization: acute not fatigue related (AI), overuse not fatigue related (OI), acute fatigue related (AFI), or overuse fatigue related (OFI). RESULTS: Overall, 360 questionnaires were completed (84% response rate). Overuse not fatigue-related injuries were encountered most often (44.7%), compared with AI (41.9%) and OFI (9.7%). Number of practices within 48 hours before injury was higher (1.7 ± 1.5) for athletes with OI versus those with AI (1.3 ± 1.4; P = 0.025). Athlete or parent perception of excessive play/training without adequate rest in the days before the injury was related to overuse (P = 0.016) and fatigue-related injuries (P = 0.010). Fatigue-related injuries were related to sleeping ≤6 hours the night before the injury (P = 0.028). CONCLUSIONS: When scheduling youth sporting events, potential activity volume and intensity over any 48-hour period, recovery time between all training and competition bouts, and potential between-day sleep time (≥ 7 hours) should be considered to optimize safety. An overscheduling injury can be defined as an injury related to excessive planned physical activity without adequate time for rest and recovery, including between training sessions/competitions and consecutive days.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".