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Record W4380574940 · doi:10.1136/bjsports-2023-106849

Introducing the Sport Concussion Assessment Tool 6 (SCAT6)

2023· editorial· en· W4380574940 on OpenAlexaff
Ruben J. Echemendía, Benjamin L. Brett, Steven P. Broglio, Gavin A Davis, Christopher C. Giza, Kevin M. Guskiewicz, Kimberly G. Harmon, Stanley A. Herring, David R. Howell, Christina L. Master, Tamara C. Valovich McLeod, Michael McCrea, Dhiren Naidu, Jon Patricios, Margot Putukian, Samuel R. Walton, Kathryn Schneider, Joel S. Burma, Jared M. Bruce

Bibliographic record

VenueBritish Journal of Sports Medicine · 2023
Typeeditorial
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsConcussionPhysical medicine and rehabilitationMedicinePhysical therapyInjury preventionHuman factors and ergonomicsPoison controlPsychologyMedical emergency

Abstract

fetched live from OpenAlex

Box 1 What's New? ⇒ Enhanced athlete demographic section.⇒ The SCAT6 is for use in adolescents (>12 years), and adults.The Child SCAT6 is for use with children 8-12 years.⇒ SCAT6 requires a minimum of 10-15 min to be performed correctly.⇒ SCAT6 is to be used within 72 hours (3 days), and up to 7 days, following injury.⇒ Revised recognise and remove section.⇒ Revised immediate assessment/ neurological screen section.⇒ New coordination and ocular/motor screen.⇒ Enhanced Red Flags section.⇒ Removal of the 'Read Aloud' instructions of the symptom scale.⇒ Removal of the immediate memory 5-word list option (10-word list included).⇒ Addition of a timed component to the months in reverse subtest.⇒ Revised coordination and balance examination, including an optional dual-task tandem gait.⇒ Revised detailed instruction section.

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)0.014
metaresearch head score (Gemma)0.054
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.020
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.054
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0060.002
Science and technology studies0.0020.003
Scholarly communication0.0100.004
Open science0.0040.002
Research integrity0.0200.025
Insufficient payload (model declined to judge)0.0090.009

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 head0.023
GPT teacher head0.354
Teacher spread0.332 · 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
GenreEditorial

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

Citations165
Published2023
Admission routes1
Has abstractno

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