Evaluation, management and prevention of lower extremity youth ice hockey injuries
Bibliographic record
Abstract
Charles A Popkin,1 Brian M Schulz,2 Caroline N Park,1 Thomas S Bottiglieri,1 T Sean Lynch1 1Department of Orthopedic Surgery, Center for Shoulder, Elbow and Sports Medicine at Columbia University, New York, NY, 2Kerlan‑Jobe Orthopedic Clinic, Los Angeles, CA, USA Abstract: Ice hockey is a fast-paced sport played by increasing numbers of children and adolescents in North America and around the world. Requiring a unique blend of skill, finesse, power and teamwork, ice hockey can become a lifelong recreational activity. Despite the rising popularity of the sport, there is ongoing concern about the high frequency of musculoskeletal injury associated with participation in ice hockey. Injury rates in ice hockey are among the highest in all competitive sports. Numerous research studies have been implemented to better understand the risks of injury. As a result, rule changes were adopted by the USA Hockey and Hockey Canada to raise the minimum age at which body checking is permitted to 13–14 years (Bantam level) from 11–12 years (Pee Wee). Continuing the education of coaches, parents and players on rules of safe play, and emphasizing the standards for proper equipment use are other strategies being implemented to make the game safer to play. The objective of this article was to review the evaluation, management and prevention of common lower extremity youth hockey injuries. Keywords: youth hockey, body checking, injury prevention, femoroacetabular impingement, apophyseal avulsions
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".