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Record W2077879868 · doi:10.1055/s-0030-1263103

Measurement of Head Impacts in Youth Ice Hockey Players

2010· article· en· W2077879868 on OpenAlexafffund
Nicholas S. Reed, Tim Taha, Michelle Keightley, Catrin Duggan, Jim McAuliffe, Jeff Cubos, Joseph Baker, Brent E. Faught, Moira McPherson, William Montelpare

Bibliographic record

VenueInternational Journal of Sports Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsLakehead UniversityBrock UniversityNipissing UniversityYork UniversityUniversity of Toronto
FundersOntario Neurotrauma Foundation
KeywordsIce hockeyHead (geology)Poison controlPhysical medicine and rehabilitationPsychologyMedicineEnvironmental healthGeology

Abstract

fetched live from OpenAlex

Despite growing interest in the biomechanical mechanisms of sports-related concussion, ice hockey and the youth sport population has not been studied extensively. The purpose of this pilot study was: 1) to describe the biomechanical measures of head impacts in youth minor ice hockey players; and, 2) to investigate the influence of player and game characteristics on the number and magnitude of head impacts. Data was collected from 13 players from a single competitive Bantam boy's (ages 13-14 years) AAA ice hockey team using telemetric accelerometers implanted within the players' helmets at 27 ice hockey games. The average linear acceleration, rotational acceleration, Gadd Severity Index and Head Injury Criterion of head impacts were recorded. A significantly higher number of head impacts per player per game were found for wingers when compared to centre and defense player positions (df=355, t=3.087, p=0.00218) and for tournament games when compared to regular season and playoff games (df=355, t=2.641, p=0.086). A significant difference in rotational acceleration according to player position (F2,1812=4.9551, p=0.0071) was found. This study is an initial step towards a greater understanding of head impacts in youth ice hockey.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.345
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.382
Teacher spread0.296 · 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

Citations54
Published2010
Admission routes2
Has abstractyes

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