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Record W2607525868 · doi:10.1136/bjsports-2017-097506

The Sport Concussion Assessment Tool 5th Edition (SCAT5): Background and rationale

2017· article· en· W2607525868 on OpenAlexaff
Ruben J. Echemendía, Willem Meeuwisse, Paul McCrory, Gavin A Davis, Margot Putukian, John J. Leddy, Michael Makdissi, S. John Sullivan, Steven P. Broglio, Martin Raftery, Kathryn Schneider, James Kissick, Michael McCrea, Jiří Dvořák, Allen K. Sills, Mark Aubry, Lars Engebretsen, Mike Loosemore, Gordon Fuller, Jeffrey S. Kutcher, Richard Ellenbogen, Kevin M. Guskiewicz, Jon Patricios, Stanley A. Herring

Bibliographic record

VenueBritish Journal of Sports Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCarleton UniversityUniversity of OttawaAlberta Children's HospitalUniversity of Calgary
FundersNational Institute for Health and Care Research
KeywordsConcussionMedicinePoison controlInjury preventionPsychologyPhysical therapyMedical educationMedical emergency

Abstract

fetched live from OpenAlex

This paper presents the Sport Concussion Assessment Tool 5th Edition (SCAT5), which is the most recent revision of a sport concussion evaluation tool for use by healthcare professionals in the acute evaluation of suspected concussion. The revision of the SCAT3 (first published in 2013) culminated in the SCAT5. The revision was based on a systematic review and synthesis of current research, public input and expert panel review as part of the 5th International Consensus Conference on Concussion in Sport held in Berlin in 2016. The SCAT5 is intended for use in those who are 13 years of age or older. The Child SCAT5 is a tool for those aged 5-12 years, which is discussed elsewhere.

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)0.039
metaresearch head score (Gemma)0.073
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.073
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.007
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.002

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 head0.061
GPT teacher head0.382
Teacher spread0.322 · 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
GenreMethods

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

Citations824
Published2017
Admission routes1
Has abstractyes

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