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Record W4386331107 · doi:10.51224/cik.2023.58

Concussion in sport – What do we know, and what’s next?

2023· article· en· W4386331107 on OpenAlexfundno aff
Christopher R. Matthews, P. Watson, Tom Dening, Ian Varley, Angus M. Hunter, Dominic Malcolm, Debi Flores, Reem AlHashmi

Bibliographic record

VenueCommunications in Kinesiology · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
FundersTrent UniversityNottingham Trent University
KeywordsConcussionSet (abstract data type)DisciplineFace (sociological concept)Engineering ethicsPsychologyPublic relationsSociologyPoison controlMedicineComputer sciencePolitical scienceEngineeringInjury preventionSocial science

Abstract

fetched live from OpenAlex

This paper presents shortened versions of talks given at a symposium about concussion in sport. The piece is designed, in similar lines as the event, to help communicate knowledge and ideas between academics in multiple disciplines, communities of practice and to develop public and patient involvement. The seven short essays are presented in the style of a conference proceedings paper. Each author presents their own focus, with one paper drawing on lived experiences and activism, three taking a biomedical approach and the remaining three drawing on social scientific analysis and literature. We have developed this paper to provide readers with a concise, but not complete, understanding of different topics related to concussion and brain injuries in sport. The limitations of space means that the authors have had to reduce the complexity of some ongoing debates on the topic. The final section is based on the comments and discussions that happened during and after the symposium. It offers some important takeaways and encourages scholars working in this area to prioritize multi-disciplinary research, and highlights the importance of centering the experiences and lives of those people affected by concussions and neurological disorders in the development and delivery of future work. It is hoped that communicating ideas in this way will encourage such ways of thinking as a means of tackling the complex set of problems that face those involved in sports where repeated forceful impact is a normalized 'part of the game'.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0070.013
Scholarly communication0.0120.016
Open science0.0020.005
Research integrity0.0070.015
Insufficient payload (model declined to judge)0.0050.003

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 head0.147
GPT teacher head0.420
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations6
Published2023
Admission routes1
Has abstractyes

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