613 MEP081 – Breaking the ice: analyzing head contacts, physical contacts, and video-suspected injury rates in community para ice hockey
Bibliographic record
Abstract
Background In the 2014 and 2018 Paralympics, para ice hockey (PIH) had the second and third highest rate of injury among all sports, respectively. However, at the community level, the rate and burden of injury are poorly understood. This is of concern as popularity of the sport continues to grow. Objective To compare physical contact (PC), head contact (HC), and video-suspected injury (SI) incidence rates (IR) based on video-analysis among three PIH levels of play. Design Cross-sectional study. Setting Canadian ice hockey rinks. Participants PIH, Junior (lowest level; ~< 16 years), Intermediate C, and Intermediate B (IC; lower level; IB; higher level, ~>16 years) tournament games from the 2021–2022 season. Assessment of Risk Factors Games were filmed and then analyzed in Dartfish, using validated criteria for HC, and trunk contacts (lower intensity PC and body checking). SI’s were identified using consensus guideline metrics. Main Outcome Measurements Multivariate Poisson regression analyses (adjusted for cluster by game, offset by game-minutes) were used to estimate IRs (per 100 team-game-minutes) and incidence rate ratios (IRR) using IC as the reference group for analyses. Results Analyses of thirty games (n=10 Junior; n=10 IC; n=10 IB) revealed, IB had a 50% higher PC IR than IC (IRR=1.50,95%CI:1.09–2.08), Junior did not differ from IC (IRR=1.15,95%CI: 0.83–1.59). The results suggest an 84% higher rate of HC in IB compared IC (IRR=1.84,95%CI: 0.83–4.09)(not significant though clinically relevant). Across all levels, 0–3% of all direct HCs were penalized. SI’s were 3.19-fold higher in IB (IR=1.78/100-team-game-minutes, 95%CI:0.09–3.61) than IC (IR=0.56/100-team-game-minutes,95%CI:0.15–2.05)(IRR= 3.19, 95%CI:0.76–13.41)(not significant but clinically relevant). Conclusions IB had the highest in-game PC IR. The rates of HC are high across all levels of play and few (0–3%) HCs were penalized in any level. These findings can inform future research targeting primary prevention strategies in this population to reduce HCs rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".