Musculoskeletal Injuries in National Hockey League Goaltenders
Bibliographic record
Abstract
BACKGROUND: Hockey goaltenders are exposed to unique in-game physical demands, different from those experienced by other positional players. While previous studies have identified the injuries experienced by elite goaltenders, these have been limited by the use of secondary data or data from small samples over relatively short time intervals. PURPOSE: To quantify the type and number of injuries occurring in National Hockey League (NHL) goaltenders and the time lost due to these injuries. STUDY DESIGN: Descriptive epidemiology study. METHODS: Data from the NHL's Athlete Health Management System was analyzed between the 2009-2010 and 2022-2023 seasons. The total number of each musculoskeletal injury, the total days missed (TDM) (a surrogate measure for injury severity), and the exposure-adjusted injury rate (EAIR) was quantified. For each individual diagnosis, the total number of injuries, mean, median, standard deviation, and minimum and maximum (range) TDM were calculated. RESULTS: There were 1585 musculoskeletal injuries recorded over 14 NHL seasons resulting in 31,406 TDM. The number of total injuries per year was stable across the data collection period. The EAIR was higher in the preseason (69.4) and postseason (46.8) compared with the regular season (17.1). The majority of the injuries occurred during games (n = 904), followed by practice and off-ice activities (n = 681). The most common mechanisms were noncontact (n = 673), incidental contact (n = 252), and "unknown/other" (n = 374). Proximal adductor injuries accounted for the largest number of injuries (n = 371), leading to the highest total TDM (n = 6126). Concussions (n = 110) and MCL sprains (n = 95) were the second and third most common injuries, respectively. CONCLUSION: The most common injuries to NHL goaltenders are proximal adductor injuries, concussions, and MCL sprains. Attention should be paid to the significant number of injuries occurring in the preseason and practice periods.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".