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Record W4387078945 · doi:10.1136/bmjsem-2023-001645

How to harness and improve on video analysis for youth rugby player safety: a narrative review

2023· review· en· W4387078945 on OpenAlexafffund
Isla Shill, Stephen West, James Brown, Fiona Wilson, Debbie Palmer, Ian Pike, Sharief Hendricks, Keith Stokes, Brent Hagel, Carolyn A. Emery

Bibliographic record

VenueBMJ Open Sport & Exercise Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsAlberta Children's HospitalBC Children's HospitalUniversity of British ColumbiaHotchkiss Brain InstituteUniversity of Calgary
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaInternational Olympic Committee
KeywordsNarrativeVideo gameEliteApplied psychologyPsychologyAdvertisingMultimediaPolitical scienceComputer scienceBusinessLaw

Abstract

fetched live from OpenAlex

Video analysis is a useful tool for injury surveillance in rugby union. There are few video analysis studies in the professional female game, with most studies published in the male elite/professional settings. Moreover, there is a sparsity of literature in youth rugby settings. The following narrative review outlines the strengths and limitations of the current video analysis literature for injury surveillance in youth rugby union, highlights the importance of video analysis for youth rugby player safety and welfare, and discusses recommendations for using video analysis to inform player safety in youth 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0140.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
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 head0.123
GPT teacher head0.454
Teacher spread0.331 · 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 designOther design
Domainnot available
GenreReview

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

Citations12
Published2023
Admission routes2
Has abstractyes

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