Time to Ring in the Body Checking, Head Contact, and Suspected Injury Rates in Youth Ringette: A Video-Analysis Study in Youth Ringette and Female Ice Hockey
Bibliographic record
Abstract
OBJECTIVE: To compare physical contacts (PCs), including head contacts (HCs), suspected concussion, and nonconcussion injury incidence rates between youth ringette and female ice hockey. DESIGN: Cross-sectional. SETTING: Alberta ice arenas. PARTICIPANTS: Players participating in 8 U16AA (ages 14-15 years) ringette and 8 U15AA (ages 13-14 years) female ice hockey games during the 2021 to 2022 season. ASSESSMENT OF RISK FACTORS: Dartfish video-analysis software was used to analyze video recordings. MAIN OUTCOME MEASURES: Univariate Poisson regression analyses (adjusted for cluster by team-game, offset by game minutes) were used to estimate PCs (including HCs) and suspected injury (concussion and nonconcussion) and concussion-specific IRs and incidence rate ratios (IRRs) to compare sports. Proportions of all PCs that were body checks (level 4-5 trunk PC) and direct HCs (HC 1 ) penalized were reported. RESULTS: Ringette had a 2.6-fold higher rate of body checking compared with hockey (IRR = 2.63, 95% CI: 1.59-4.37). Ringette also had a 2-fold higher rate of HC 1 compared with hockey (IRR = 2.08, 95% CI: 1.37-3.16). A 3.4-fold higher rate of suspected injury was found in ringette (IRR = 3.37, 95% CI: 1.40-8.15). There was no significant difference in suspected concussion IRs in ringette compared with hockey (IRR = 1.93, 95% CI: 0.43-8.74). Despite being prohibited in both sports, only a small proportion of body checks (Ringette = 18%; Hockey = 17%) and HC 1 (Ringette = 6%; Hockey = 6%) were penalized. CONCLUSIONS: Higher rates of body checking, HC 1 , and suspected injuries were found in ringette compared with female ice hockey. Body checking and HC 1 were rarely penalized, despite rules disallowing them in both sports. Future research should consider other youth age groups.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".