MétaCan
Menu
Back to cohort

Characteristics of potential concussive events in three elite football tournaments

2019· article· en· W2963013227 on OpenAlexafffund
Nicholas Armstrong, Mario Rotundo, Jason Aubrey, Christopher Tarzi, Michael D. Cusimano

Bibliographic record

VenueInjury Prevention · 2019
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchOntario Neurotrauma Foundation
KeywordsConcussionFootballPoison controlInjury preventionHead injuryFootball playersPsychologyPhysical therapyMedicinePhysical medicine and rehabilitationMedical emergencyGeographySurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: Identify patterns in the nature and characteristics of potential concussive events (PCEs) in football. METHODS: This study analysed the incidence and characteristics of PCEs that occurred during the 2014 and 2018 Fédération Internationale de Football Association World Cups, and the 2016 UEFA Euro Cup. PCEs were defined as direct head collision incidents resulting in the athlete being unable to immediately resume play for at least 5 sec following impact. RESULTS: A total of 218 incidents were identified in 179 matches (1.22 per match, 36.91 per 1000 hours of exposure). The most common mechanism of PCE was elbow-to-head (28.7%, n=68). The frontal region was the most frequently affected location of impact with 22.8% (n=54). CONCLUSION: Our study defined the identification, prevalence and nature of PCEs in professional international soccer tournaments. Our findings indicate the different contexts and mechanisms of head contact and contact to different regions of the head can be associated with varying signs of concussion. The results highlight targets for future injury prevention 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 score0.199
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

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

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 head0.031
GPT teacher head0.341
Teacher spread0.311 · 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

Citations17
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueInjury PreventionSame topicTraumatic Brain Injury ResearchFrench-language works237,207