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Record W2918473677 · doi:10.1080/10255842.2019.1577827

The biomechanics of concussion for ice hockey head impact events

2019· article· en· W2918473677 on OpenAlexaff
Andrew Post, T. Blaine Hoshizaki, Clara Karton, J. Michio Clark, Lauren Dawson, Janie Cournoyer, Karen Taylor, Anna Oeur, Michael D. Gilchrist, Michael D. Cusimano

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2019
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoUniversity of OttawaSt. Michael's Hospital
Fundersnot available
KeywordsConcussionIce hockeyBiomechanicsPhysical medicine and rehabilitationPoison controlHead injuryInjury preventionPhysical therapyMedicineSurgeryMedical emergencyAnatomy

Abstract

fetched live from OpenAlex

The purpose of this research was to conduct reconstructions of concussive and non-concussive impacts in ice hockey to determine the biomechanics and thresholds of concussive injury in ice hockey. Videos of concussive and non-concussive impacts in an elite professional ice hockey league in North America were reconstructed using physical and finite element model methods. Eighty concussive and 45 non-concussive events were studied. Logistic regressions indicate significant thresholds for concussion for linear/rotational acceleration and CSDM10%. Impacts in ice hockey were mostly long duration events, longer than 15 ms. These results have significant implications for helmet standards and development to prevent concussion.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.054
GPT teacher head0.435
Teacher spread0.381 · 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 designSimulation or modeling
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

Citations43
Published2019
Admission routes1
Has abstractyes

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