Oral injuries related to Ice Hockey in the province of Alberta, Canada: Trends over the last 15 years
Bibliographic record
Abstract
BACKGROUND/AIMS: Ice hockey players of all ages experience oral and dental injuries. The aim of this study was to evaluate the rates of ice hockey-related oral injuries, time lost due to oral injury, and mechanisms of oral injuries in the province of Alberta during a 15-year period (2001-2016). METHODS: Hockey Alberta, the governing body for minor ice hockey associations across the province, collects injury report forms from injured participants in sanctioned events. Fifteen years (2001-2016) of this database was examined for total respondents suffering oral injuries. Data on total injuries, estimated time lost, and injury mechanism were analyzed. RESULTS: Overall, 12 433 ice hockey-related injuries were recorded. The oral region was the third most common body part (16% of total injuries) to be injured after the arms and legs. Oral injuries have been occurring at a relatively constant rate each year from 2001 to 2016, with a maximum of 174 and minimum of 99 reported. Oral injuries usually result in a short absence from the sport of 1 week or less and tend to occur through being struck by a stick or the hockey puck. This differs from total injuries, which tend to occur through collisions with the boards or other players. CONCLUSIONS: Rates of oral injuries in Alberta due to ice hockey comprise a significant portion of the injuries that players sustain. Oral injuries occur mostly when a player is struck with a puck or stick, and the rest of the body is injured primarily through collisions. Dental practitioners can help ice hockey athletes prevent oral and dental injuries through encouraging the use of mouthguards (custom over boil and bite) and continuing to wear full-face protection.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".