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Record W3120302636 · doi:10.1007/s11682-020-00423-2

Coordinating Global Multi-Site Studies of Military-Relevant Traumatic Brain Injury: Opportunities, Challenges, and Harmonization Guidelines

2021· review· en· W3120302636 on OpenAlexaff
David F. Tate, Emily L. Dennis, John T Adams, Maheen M. Adamson, Heather G. Belanger, Erin D. Bigler, Heather C. Bouchard, Alexandra L. Clark, Lisa Delano‐Wood, Seth G. Disner, Blessen C. Eapen, Carol E. Franz, Elbert Geuze, Naomi J. Goodrich‐Hunsaker, Kihwan Han, Jasmeet P. Hayes, Sidney R. Hinds, Cooper B. Hodges, Elizabeth S Hovenden, Andrei Irimia, Kimbra Kenney, Inga K. Koerte, William S. Kremen, Harvey S. Levin, Hannah M. Lindsey, Rajendra A. Morey, Mary R. Newsome, John Ollinger, Mary Jo Pugh, Randall S. Scheibel, Martha E. Shenton, Danielle R. Sullivan, Brian Taylor, Maya Troyanskaya, Carmen Vélez, Benjamin Wade, Xin Wang, Ashley L. Ware, Ross Zafonte, Paul M. Thompson, Elisabeth A. Wilde

Bibliographic record

VenueBrain Imaging and Behavior · 2021
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Calgary
FundersDefense and Veterans Brain Injury CenterNational Center for Advancing Translational SciencesNational Institute of Biomedical Imaging and BioengineeringNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthNational Institute on AgingNational Institutes of HealthU.S. Department of Veterans AffairsU.S. Department of Defense
KeywordsTraumatic brain injuryNeuroimagingPopulationMedicineNeuropsychologyPsychologyNeurosciencePsychiatryCognition

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.044
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.052
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0060.006
Science and technology studies0.0010.002
Scholarly communication0.0060.006
Open science0.0070.004
Research integrity0.0050.005
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 head0.501
GPT teacher head0.497
Teacher spread0.004 · 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.

Study designNot applicable
DomainMethods
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

Citations20
Published2021
Admission routes1
Has abstractno

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