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Mechanisms of Injury for Concussions in University Football, Ice Hockey, and Soccer

2006· article· en· W2025321677 on OpenAlexafffundabout
J. Scott Delaney, Vishal Puni, Fabrice Douglas Rouah

Bibliographic record

VenueClinical Journal of Sport Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersMcGill University
KeywordsConcussionFootballIce hockeyAthletesMedicineAmerican footballPhysical therapyInjury preventionPoison controlHead injuryPhysical medicine and rehabilitationMedical emergencySurgeryGeography

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the mechanisms of injury for concussions in university football, ice hockey, and soccer. DESIGN: Prospective analysis. SETTING: McGill University. PATIENTS: All athletes participating in varsity football, ice hockey, and soccer. MAIN OUTCOME MEASURES: Athletes participating in university varsity football, ice hockey, and soccer were followed prospectively to determine the mechanisms of injury for concussions, whether certain mechanisms of injury causing concussions were more common in any of the three sports, whether different areas of the body seem to be more vulnerable to a concussion after contact, and whether these areas might be predisposed to higher grades of concussion after contact. RESULTS: There were 69 concussions in 60 athletes over a 3-year period. Being hit in the head or helmet was the most common mechanism of injury for all 3 sports. The side/temporal area of the head or helmet was the most probable area to be struck, resulting in concussion for both football and soccer. When examining the body part or object delivering the concussive blow, contact with another player's helmet was the most probable mechanism in football. CONCLUSION: The mechanisms of injury for concussions in football are similar to previously published research on professional football players. The mechanisms of injury for concussions in soccer are similar to past research on Australian rules football and rugby.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.150
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.090
GPT teacher head0.419
Teacher spread0.329 · 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

Citations130
Published2006
Admission routes3
Has abstractyes

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