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Record W2801403267 · doi:10.1089/neu.2018.5643

National Institute of Neurological Disorders and Stroke and Department of Defense Sport-Related Concussion Common Data Elements Version 1.0 Recommendations

2018· article· en· W2801403267 on OpenAlexaff
Steven P. Broglio, Anthony P. Kontos, Harvey S. Levin, Kathryn Schneider, Elisabeth A. Wilde, Robert C. Cantu, Nina Feddermann‐Demont, Gordon Fuller, Isabelle Gagnon, Gérard A. Gioia, Christopher C. Giza, Grace S. Griesbach, John J. Leddy, Michael L. Lipton, Andrew R. Mayer, Thomas W. McAllister, Michael McCrea, Lara B. McKenzie, Margot Putukian, Stefano Signoretti, Stacy J. Suskauer, Robert F. Tamburro, Michael S. Turner, Keith Owen Yeates, Roger Zemek, Sherita Alai, Joy Esterlitz, Katelyn Gay, Patrick S.F. Bellgowan, Kristen Joseph

Bibliographic record

VenueJournal of Neurotrauma · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsTraffic Injury Research FoundationChildren's Hospital of Eastern OntarioUniversity of CalgaryMcGill UniversityMontreal Children's HospitalMcGill University Health Centre
FundersNational Institute on Drug AbuseNational Institute for Health and Care Research
KeywordsConcussionStroke (engine)Traumatic brain injuryMedicinePhysical medicine and rehabilitationInjury preventionPoison controlPhysical therapyMedical emergencyPsychiatryEngineering

Abstract

fetched live from OpenAlex

Through a partnership with the National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, and Department of Defense, the development of Sport-Related Concussion (SRC) Common Data Elements (CDEs) was initiated. The aim of this collaboration was to increase the efficiency and effectiveness of clinical research studies and clinical treatment outcomes, increase data quality, facilitate data sharing across studies, reduce study start-up time, more effectively aggregate information into metadata results, and educate new clinical investigators. The SRC CDE Working Group consisted of 32 worldwide experts in concussion from varied fields of related expertise divided into three Subgroups: Acute (<72 h post-concussion), Subacute (3 days-3 months post-concussion) and Persistent/Chronic (>3 months post-concussion). To develop CDEs, the Subgroups reviewed various domains, then selected from, refined, and added to existing CDEs, case report forms and field-tested data elements from national registries and funded research studies. Recommendations were posted to the NINDS CDE Website for Public Review from February 2017 to April 2017. Following an internal Working Group review of recommendations, along with consideration of comments received from the Public Review period, the first iteration (Version 1.0) of the NINDS SRC CDEs was completed in June 2017. The recommendations include Core and Supplemental-Highly Recommended CDEs for cognitive data elements and symptom checklists, as well as other outcomes and end-points (e.g., vestibular, oculomotor, balance, anxiety, depression), and sample case report forms (e.g., injury reporting, demographics, concussion history) for domains typically included in clinical research studies. The NINDS SRC CDEs and supporting documents are publicly available on the NINDS CDE website www.commondataelements.ninds.nih.gov . Widespread use of CDEs by researchers and clinicians will facilitate consistent SRC clinical research and trial design, data sharing, and metadata retrospective analysis.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.146
GPT teacher head0.402
Teacher spread0.257 · 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.

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

Citations109
Published2018
Admission routes1
Has abstractyes

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