Bibliographic record
Abstract
This study aims to identify and quantify injury diagnoses, locations, mechanisms, and trends for a Canadian Intercollegiate Athletic Union (CIAU) male ice hockey team between the 1984--85 and 2000--2001 seasons. Injury rates were assessed by type, position, period, zone, game type, and time loss. Also described are the interactions between injury location and diagnosis, as well as the relationships between time loss, injury location, and diagnosis. This study was based on injury reports provided by the ice hockey staff, which followed a standard injury reporting protocol over the last 17 years. The injury rate of 18.9 injuries per 1000 player hours, third highest among intercollegiate sports, increased slightly over the period covered. The shoulder, face, thigh, and knee were the most injured areas. The number of lacerations decreased, while contusions, sprains, and strains increased, causing the most time loss. Concussions represented the fourth greatest cause of time loss although it accounted for 6.6% of diagnosed injuries. Checking was by far the greatest cause of injury. The examination of the anatomical location-diagnosis interaction revealed that most lacerations affected the face (chin), instability-related injuries (i.e. sprains, strains, and dislocations) involved mostly the knee, groin, and shoulder, respectively. Fractures were localized mostly to the fingers. It seems, therefore, that certain anatomical locations are more susceptible to specific injury types. The fact that the injury rate increased over time may be an indication of the increasing intensity of the game, players' aggressive attitudes and behaviors in response to equipment innovations, or utilization beyond design limitations. Players already have a gladiator-like appearance, and unless we choose to define hockey as a gladiator sport, some important rule, attitude, and equipment design changes will have to be implemented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".