Incidence and Injury Characteristics of Medial Collateral Ligament Injuries in Male Collegiate Ice Hockey Players
Bibliographic record
Abstract
BACKGROUND: Medial collateral ligament (MCL) injuries are the second most common injury resulting in player lost time in elite-level ice hockey. PURPOSE: To determine the incidence and injury characteristics of knee MCL sprain in male collegiate ice hockey players. STUDY DESIGN: Case control. METHODS: Athlete exposure data demographics, mechanism of injury, player position, time of injury occurrence (game vs practice), grade of MCL sprain, concomitant injuries, and lost time for cases were extracted from a computerized injury database of 8 college hockey seasons at 1 university. MCL injury rates were calculated. Injury characteristics were descriptively summarized. Simple linear regression was utilized to determine the relationship between the grade of MCL injury and player lost time. RESULTS: There were 13 MCL injuries in 10 players. The overall incidence rate was 0.44 injuries per 1000 athlete exposures. Two players suffered reinjuries. Defensemen and forwards were equally represented. Contact with another player or the ice was the mechanism of injury in 77% of players. Grade 2 injuries were most common. The grade of injury predicted time lost from play (P < 0.01). CONCLUSION AND CLINICAL RELEVANCE: The lost time relates directly to the severity of injury.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".