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Record W4392350680 · doi:10.1136/bjsports-2024-ioc.112

808 BO36 – On thin ice: high injury and concussion rates in Canadian adolescent ringette

2024· article· en· W4392350680 on OpenAlexaffabout
Emily E Heming, Cheryl Barnabé, Kelly Russell, Debbie Palmer, Kathryn Schneider, Carolyn A. Emery

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicDiverse Approaches in Healthcare and Education Studies
Canadian institutionsSpinal Cord Injury AlbertaAlberta Children's HospitalResearch CanadaUniversity of ManitobaHotchkiss Brain InstituteChildren's Hospital Research Institute of ManitobaAlberta Bone and Joint Health InstituteUniversity of Calgary
Fundersnot available
KeywordsConcussionRate ratioPoisson regressionMedicinePoison controlInjury preventionIncidence (geometry)DemographyPhysical therapyEmergency medicineConfidence intervalPopulationInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

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.

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)0.001
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 score0.017
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.049
GPT teacher head0.359
Teacher spread0.310 · 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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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