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Record W2016139200 · doi:10.1080/14763141.2015.1025236

Defining the effective impact mass of elbow and shoulder strikes in ice hockey

2015· article· en· W2016139200 on OpenAlex
Philippe Rousseau, T. Blaine Hoshizaki

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSports Biomechanics · 2015
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsImpactOntario Neurotrauma FoundationUniversity of Ottawa
Fundersnot available
KeywordsIce hockeyElbowConcussionPercentileHybrid IIIFootballPoison controlImpactMathematicsPhysical medicine and rehabilitationInjury preventionMedicineEngineeringStructural engineeringStatisticsGeographySurgery

Abstract

fetched live from OpenAlex

Reconstruction of real-life events can be used to investigate the relationship between the mechanical parameters of the impact and concussion risk. Striking mass has typically been approximated as being the mass of the body part coming into contact with the head without accounting for the force applied by the striking athlete. Thus, the purpose of this study was to measure the effective impact mass of three common striking techniques in ice hockey. Fifteen participants were instructed to strike a suspended 50th percentile Hybrid III headform at least three times with their elbow or shoulder. Effective impact mass was calculated by measuring the change in velocity of the player and the headform. Mean effective impact mass for the extended elbow, tucked-in elbow, and shoulder check conditions were 4.8, 3.0, and 12.9 kg, respectively. Peak linear accelerations were lower than the values associated with concussion in American football which could be a reflection of the methodology used in this study as well as inherent differences between both sports.

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.

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 score0.493
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.036
GPT teacher head0.351
Teacher spread0.315 · 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