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Record W6338109 · doi:10.1177/31.4.6338109

Quantifying and Comparing the Head Impact Biomechanics of Different Player Positions for Canadian University Football

2014· article· en· W6338109 on OpenAlexfundaboutno aff
Kody R. Campbell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConcussionFootballBiomechanicsPhysical medicine and rehabilitationFootball playersAngular accelerationPoison controlHead (geology)Physical therapyInjury preventionPsychologyMedicineAccelerationMedical emergencyAnatomyHistory

Abstract

fetched live from OpenAlex

Differences between Canadian and American football could affect the magnitudes of head impacts and risk of concussion to Canadian players. This study sought to quantify and compare the number, magnitude, and location of impacts that Canadian University football players of different positions experienced during games and practice in a season. A kinematic measuring device collected the linear accelerations and rotational velocities of the head from impacts experienced by players competing in practices and games. The impact magnitudes that were experienced in games were significantly larger than in practice. The offensive back position and wide receiver position had significantly larger peak linear and rotational accelerations than the offensive linemen position. The magnitudes of impacts experienced by the wide receiver position in Canadian football were larger and not consistent with previous American studies, likely due to the pass style offence that is common in Canadian football. We observed that the head impact magnitudes vary by position, and session type in Canadian football.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.571

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.169
GPT teacher head0.372
Teacher spread0.204 · 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 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

Citations0
Published2014
Admission routes2
Has abstractyes

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