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Record W4321164844 · doi:10.1007/s11845-023-03307-8

Characteristics of potential concussive events in elite hurling: a video-analysis study

2023· article· en· W4321164844 on OpenAlexaff
Mario Rotundo, Darek Sokol-Randell, Chris Bleakley, Paul Donnelly, Gregory Tierney

Bibliographic record

VenueIrish Journal of Medical Science (1971 -) · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsMemorial University of NewfoundlandUniversity of British Columbia
Fundersnot available
KeywordsConcussionEliteMedicinePhysical medicine and rehabilitationPsychologyPoison controlInjury preventionMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: High-impact sports such as hurling place participants at risk of sport-related concussion (SRC). AIMS: This study will evaluate the characteristics of potential concussive events (PCEs) that occur in elite male hurling to acquire an understanding of how they occur. METHODS: The authors recorded PCEs and their characteristics throughout two seasons of inter-county GAA competition using broadcast footage based on a previously validated protocol. RESULTS: A total of 183 PCEs were identified over 82 inter-county matches (2.23 per match; 59.5 per 1000 h of exposure). PCEs that occurred in the 4th quarter were significantly more likely to result in signs of SRC. Players most often intended to receive/control the sliotar (36.4%, n = 64) prior to PCEs. The most frequently observed mechanism was shoulder-to-head (20.2%, n = 37). Impacts to the lateral aspect of the head were 2.7 times more likely to result in visible signs than impacts to anteroposterior regions. CONCLUSIONS: Players appear to be at a higher risk of SRC later in the match or when receiving the sliotar. Strikes to the lateral aspect of the head and those involving the shoulder appear to produce severe events. These findings provide initial guidance for the development of targeted player protection 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)0.015
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.125
GPT teacher head0.448
Teacher spread0.323 · 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.

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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueIrish Journal of Medical Science (1971 -)Same topicTraumatic Brain Injury ResearchFrench-language works237,207