Anterior Cruciate Ligament Injuries in the National Hockey League: Epidemiology and Performance Impact
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of anterior cruciate ligament (ACL) injuries in the National Hockey League (NHL) and to examine the effects of this injury on return-to-play status and performance. DESIGN: Case series; level of evidence, 4. METHODS: This was a 2-phase study. Phase I used the NHL electronic injury surveillance system and Athlete Health Management System to collect data on ACL injuries and man games lost over 10 seasons (2006/2007-2015/2016). Data collected in phase I were received in deidentified form. Phase II examined the performance impact of an ACL injury. Players were identified through publically available sources, and performance-related statistics were analyzed. Data collected in phase II were not linked to data collected in phase I. A paired t test was used to determine any difference in the matching variables between controls and cases in the preinjury time period. A General linear model (mixed) was used to determine the performance impact. RESULTS: Phase I: 67 ACL injuries occurred over 10 seasons. The incidence for all players was 0.42/1000 player game hours (forward, 0.61; defenseman, 0.32, goalie, 0.08) and by game exposure was 0.2/1000 player game exposures (forward, 0.33; defenseman, 0.11; goalie, 0.07). Forwards had a greater incidence rate of ACL tears with both game hours and game exposures when compared with defensemen and goalies (P < 0.001, <0.001; P = 0.008, <0.001, respectively). Phase II: 70 ACL tears (60 players) were identified. Compared with controls, players who suffered an ACL tear demonstrated a decrease in goals/season (P < 0.04), goals/game (P < 0.015), points/season (0.007), and points/game (0.001). Number of games and seasons played after an ACL injury did not differ compared with controls (P = 0.068, 0.122, respectively). CONCLUSIONS: Anterior cruciate ligament injuries occur infrequently, as it relates to other hockey injuries. Despite a high return to play, the performance after an ACL injury demonstrated a decrease in points and goals per game and per season.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".