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Record W4324018481 · doi:10.1080/14763141.2023.2186941

A video analysis examination of the frequency and type of head impacts for player positions in youth ice hockey and FE estimation of their impact severity

2023· article· en· W4324018481 on OpenAlexaff
Jessica Butterfield, Andrew Post, Clara Karton, Michael A. Robidoux, Michael D. Gilchrist, T. Blaine Hoshizaki

Bibliographic record

VenueSports Biomechanics · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIce hockeyHead (geology)Poison controlHybrid IIIPhysical medicine and rehabilitationMedicineGeology

Abstract

fetched live from OpenAlex

This research employed head impact frequency and frequency of estimated strain to analyse the influence of player position on brain trauma in U15 and U18 youth ice hockey. The methods involved a video analysis of 30 U15 and 30 U18 games where frequency, type of head impact event, and player position during impact was recorded. These impacts were then simulated in the laboratory using physical reconstructions and finite element modelling to determine the brain strains for each impact category. U15 forwards experienced significantly higher head impact frequencies (139) when compared to defenceman (50), with goalies showing the lowest frequency (6) (p < 0.05). U18 forwards experienced significantly higher head impact frequencies (220) when compared to defenceman (92), with goalies having the least frequent head impacts (4) (p < 0.05). The U15 forwards had a significantly higher frequency of head impacts at the very low and med strains and the U18s had higher frequency of head impacts for the very low and low level strains (p < 0.05). Game rule changes and equipment innovation may be considered to mitigate the increased risk faced by forwards compared to other positions in U15 and U18 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.069
GPT teacher head0.364
Teacher spread0.295 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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