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Record W4392350426 · doi:10.1136/bjsports-2024-ioc.1

703 FO01 – From rink to prevention and recovery: investigating upper extremity injuries in Canadian youth ice hockey

2024· article· en· W4392350426 on OpenAlexaffabout
Eric Gibson, Paul Eliason, Jean‐Michel Galarneau, Stephen West, Kati Pasanen, Carolyn A. Emery

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsHotchkiss Brain InstituteAlberta Bone and Joint Health InstituteSouth Health CampusOntario Brain InstituteAlberta Children's Hospital
Fundersnot available
KeywordsIce hockeyPoisson regressionInjury preventionMedicinePoison controlPhysical therapyRate ratioDemographyPhysical medicine and rehabilitationEmergency medicinePopulationEnvironmental health

Abstract

fetched live from OpenAlex

Background Youth ice hockey is a popular, fast-paced, collision sport with a high participation rate. Objective To describe upper extremity (UE) injury rates, types, severity, mechanisms, and risk factors in youth ice hockey players. Design Secondary analysis of data from a 5-year prospective cohort study (2013–2018). Setting Canadian youth ice hockey. Participants Overall, 6584 player-seasons (representing 4418 individual players) participated. Assessment of Risk Factors A multivariable mixed-effects Poisson regression model (clustering by team and offset by exposure hours) examined potential risk factors for UE injury including body checking policy, player weight, biological sex, history of injury in the past 12 months, and level of play. Main Outcome Measures Validated injury surveillance data was collected. The injury outcome for this analysis consisted of primary upper extremity injuries. Injury rates (IR) with 95% CI were estimated using Poisson regression. Results During this period, 234 UE game-related and 35 practice-related injuries were reported. The UE IR was 0.91 injuries/1000 game-hours (95% CI 0.78–1.08). Most injuries (n=188/234, 71%) resulted in >7 days of time-loss and 106/188 (40%) resulted in >28 days of time-loss. An 83% lower IR was associated with policy prohibiting bodychecking compared to leagues allowing bodychecking [IRR=0.17 (95% CI 0.09–0.29)]. A higher upper extremity IR was seen for those who reported any injury in the last 12-months compared to those with no history [IRR=1.85 (95% CI 1.30–2.55)]. There were no associations found between UE IR and player weight, biological sex, or level of play. Conclusions UE injuries typically result in >7 days time-loss and 40% >28 days. Risk factors for UE injury included participation in a body checking league, and recent history of any injury. This study serves to reinforce the need for the investigation of UE injury prevention strategies to make youth ice hockey a safer space.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.019
GPT teacher head0.285
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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