Injury Rates in House League, Select, and Representative Youth Ice Hockey
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine injury rates in a youth ice hockey program over two seasons (2002-2004). Injury rates for age groups (4-18 yr) and for different levels of competition were compared. Another purpose was to determine the effect of body checking on injury rates among these youths. METHODS: A prospective injury report form was completed by a volunteer trainer for each injury that caused a loss of player time and resulted in evaluation by a physician. The injury form documented age group, type of injury, length of time that the player missed action due to the injury, location of the injury, and circumstances that led to the injury. Participants included 2632 boys aged 4-18 who played in the 2002-2003 season and 2639 boys who played in the 2003-2004 season. RESULTS: Injuries were four times more likely to occur in games than practices. Boys who played in the most advanced levels of competition are 6.1 times more likely to be injured than boys playing in house leagues. Injury rates during games showed a trend toward increasing with the age of the player. Injury rates spiked the first year that body checking was introduced in two different competition levels. Injury rates also spiked with the onset of adolescence (age 13). CONCLUSION: The study findings suggest that the introduction of body checking at age 9 to competitive youth hockey causes an immediate but relatively short-term increase in injury rates. The period of adjustment that accompanies body checking should be taken into account when determining the age at which body checking is introduced.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".