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Record W3043022675 · doi:10.1177/2059700220911285

Comparing concussion rates as reported by hockey Canada with head contact events as observed across minor ice-hockey age categories

2020· article· en· W3043022675 on OpenAlexafffundabout
Michael A. Robidoux, Marshall Kendall, Yannick Laflamme, Andrew Post, Clara Karton, T. Blaine Hoshizaki

Bibliographic record

VenueJournal of Concussion · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConcussionIce hockeyAthletesHead injuryObservational studyInjury preventionPsychologyPhysical therapyPoison controlMedicinePhysical medicine and rehabilitationMedical emergencyPsychiatry

Abstract

fetched live from OpenAlex

Head injuries in elite and youth sport have garnered growing public attention in part because of high-profile cases of professional athletes suffering career-ending/threatening concussions and because of the increase in medical studies identifying how repeated concussive events can lead to long-term health problems, most notably degenerative brain disease. Public concerns around youth ice hockey are intensifying in light of recent evidence which suggests that effects of head injury are worse for youth than they are for athletes in later stages of life. To better understand concussion injury rate trends across all levels of youth hockey, this paper provides a retrospective analysis of concussion related hockey injury as recorded in Hockey Canada’s Injury Reporting System from the period covering 2009 to 2016, combined with two years of observational research documenting head contact events in minor hockey in the Ottawa and Gatineau regions of Ontario and Quebec. By comparing two different data sets through different methodological designs, it provides important insight into the levels of head contact in youth hockey, how head contact is occurring, and offers commentary about the levels of risk players are exposed to in minor hockey in Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.050
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.116
GPT teacher head0.365
Teacher spread0.248 · 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 teacher head, 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

Citations8
Published2020
Admission routes3
Has abstractyes

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