MétaCan
Menu
Back to cohort
Record W1595283155 · doi:10.3171/2014.8.jns132804

The Sport Concussion Education Project. A brief report on an educational initiative: from concept to curriculum

2014· article· en· W1595283155 on OpenAlexafffund
Paul S. Echlin, Andrew M. Johnson, Jeffrey D. Holmes, Annalise Michelle Tichenoff, Sarah A. O. Gray, Heather Gatavackas, Joanne L. Walsh, Tim Middlebro, Angelique Blignaut, Martin MacIntyre, C. B. C. Anderson, Eli Fredman, Michael Mayinger, Elaine N. Skopelja, Takeshi Sasaki, Sylvain Bouix, Ofer Pasternak, Karl G. Helmer, Inga K. Koerte, Martha E. Shenton, Lorie A. Forwell

Bibliographic record

VenueJournal of neurosurgery · 2014
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsFowler Kennedy Sport Medicine ClinicWestern University
FundersNational Center for Research ResourcesNational Institute of Biomedical Imaging and BioengineeringNational Institute of Mental HealthOntario Ministry of Health and Long-Term CareJapan Society for the Promotion of ScienceOntario Trillium FoundationNational Institutes of HealthNational Alliance for Research on Schizophrenia and DepressionOntario Neurotrauma FoundationMinistère de l’Éducation, Gouvernement de l’OntarioState University of New YorkU.S. Department of Defense
KeywordsRubricConcussionMedicineCurriculumMedical educationPopulationIdentification (biology)Poison controlInjury preventionPedagogyMedical emergencyPsychologyEnvironmental health

Abstract

fetched live from OpenAlex

Current research on concussion is primarily focused on injury identification and treatment. Prevention initiatives are, however, important for reducing the incidence of brain injury. This report examines the development and implementation of an interactive electronic teaching program (an e-module) that is designed specifically for concussion education within an adolescent population. This learning tool and the accompanying consolidation rubric demonstrate that significant engagement occurs in addition to the knowledge gained among participants when it is used in a school curriculum setting.

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)0.006
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.053
GPT teacher head0.386
Teacher spread0.333 · 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
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

Citations20
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueJournal of neurosurgerySame topicTraumatic Brain Injury ResearchFrench-language works237,207