808 BO36 – On thin ice: high injury and concussion rates in Canadian adolescent ringette
Bibliographic record
Abstract
Background Ringette is a popular female team ice-sport in Canada, USA, Scandinavia, and Czech Republic. Although bodychecking (i.e., intensive contact with another player to remove them from offensive control of play) is prohibited, high rates of bodychecking and suspected-injury have been reported based on video-analysis. Understanding injury and concussion rates in adolescent ringette is crucial for improving player safety. Objective To examine injury and concussion rates and types in adolescent ringette (ages 11–18). Design Cohort study. Setting Canada Participants Youth ringette players in Calgary participating in three age divisions (under-14[U14], under-16[U16], under-19[U19]) during the 2022–23 season. Assessment of Risk Factors Injury and concussion incidence rates (IR) were examined across three age groups. Main Outcome Measurements Injuries included were all complaint injuries. Poisson regression (cluster-adjusted by team) estimated injury and concussion rates (IR=#injuries/100 players/season), with incidence rate ratios (IRR) comparing IRs by age group. Injury types, player position, mechanisms, and median time-loss are reported. Results The overall injury-IR was 35.88/100 players/season (95%CI: 27.45–46.91) and concussion-specific IR was 12.94/100 players/season (95%CI: 8.42–19.89). Compared to U14(IR=27.27/100 players/season,95%CI;14.47–51.42), U16(IR=27.85,95%CI;17.65–43.94) had similar injury-IRs (IRR[U16vsU14]=1.02,95%CI;0.47–2.22) and U19(IR=64.86,95%CI;44.61–94.31) had higher injury-IRs (IRR[U19vsU14]=2.38,95%CI;1.15–4.93). The median time-loss for concussions was 18 days (IQR:20) and non-concussions was 7 days (IQR:13). Head and neck injuries were most common (40%), followed by knee (23%). Forwards sustained the greatest proportion of concussions (59%) and non-concussions (50%) reported. Game-related concussions (91%) and non-concussions (77%) accounted for most injuries, compared to practice-related. Unintentional player-contact was the primary concussion mechanism (55%). Intentional player contact (26%), unintentional player-contact (26%), and contact with the environment (26%) were the primary mechanisms reported for non-concussion injuries. Conclusion Injury and concussion rates are high among adolescent ringette players in Canada, the highest in U19 players. Continued efforts to enhance player safety and prevent injuries in ringette are needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".