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Record W2510259060 · doi:10.1123/ijatt.2016-0005

Frequency and Magnitude of Head Accelerations in a Canadian Interuniversity Sport Football Team’s Training Camp and Season

2016· article· en· W2510259060 on OpenAlexaffabout
Daniel P. Muise, Sasho MacKenzie, Tara M. Sutherland

Bibliographic record

VenueInternational Journal of Athletic Therapy & Training · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsConcussionOffensiveFootballCollege footballAmerican footballFootball playersPsychologyPhysical medicine and rehabilitationPhysical therapyInjury preventionPoison controlMedicineMedical emergencyEngineeringGeographyOperations research

Abstract

fetched live from OpenAlex

The increased awareness of concussion in sport has led to an examination of head impacts and the associated biomechanics that occur during these sporting events. The high rate of concussions in football makes it particularly relevant. 1 The purpose of this study was to examine how frequently, and to what magnitude, Canadian University football players get hit in training camp and how this compares to practices and games in regular season. An ANOVA with repeated measures indicated that, on average, players were hit significantly more in games (45.2 hits) than training camp sessions (17.7 hits) and practices (8.0 hits), while training camp was associated with significantly more hits than practices ( p < .001, η 2 = .392). Multiple positional differences were found. In particular, significantly more hits were experienced by offensive linemen (36.7 hits) and defensive linemen (31.6 hits) compared with all other positions ( p < .001, η 2 = .247). Study outcomes determined players/positions most at risk for concussion due to head impacts, which is beneficial in forming concussion prevention and assessment strategies.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.103
GPT teacher head0.349
Teacher spread0.246 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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