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Chronic Stroke Sensorimotor Impairment Is Related to Smaller Hippocampal Volumes: An ENIGMA Analysis
Artemis Zavaliangos‐Petropulu, Bethany Lo, Miranda R. Donnelly, Nicolas Schweighofer, Keith R. Lohse, Neda Jahanshad, Giuseppe Barisano, Nerisa Banaj, Michael R. Borich, Lara A. Boyd, Cathrin M. Buetefisch, Winston D. Byblow, Jessica M. Cassidy, Charalambos C. Charalambous, Adriana Bastos Conforto, Julie A. DiCarlo, Adrienne N. Dula, Natalia Egorova, Mark R. Etherton, Wuwei Feng, Kelene A. Fercho, Fatemeh Geranmayeh, Colleen A. Hanlon, Kathryn S. Hayward, Brenton Hordacre, Steven A. Kautz, Mohamed Salah Khlif, Hosung Kim, Amy Kuceyeski, David J. Lin, Jingchun Liu, Martín Lotze, Bradley J. MacIntosh, John L. Margetis, Feroze B. Mohamed, Fabrizio Piras, Ander Ramos‐Murguialday, Kate Revill, Pamela Roberts, Andrew D. Robertson, Heidi M. Schambra, Na Jin Seo, Mark S. Shiroishi, Cathy M. Stinear, Surjo R. Soekadar, Gianfranco Spalletta, Myriam Taga, Gregory Thielman, Daniela Vecchio, Nick Ward, Lars T. Westlye, Emilio Werden, Carolee J. Winstein, George F. Wittenberg, Steven L. Wolf, Kristin A. Wong, Chunshui Yu, Amy Brodtmann, Steven C. Cramer, Paul M. Thompson, Sook‐Lei Liew
Bibliographic record
VenueJournal of the American Heart Association · 2022
Typereview
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of WaterlooUniversity of TorontoOntario Brain InstituteSunnybrook Health Science CentreUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of General Medical SciencesMelbourne School of Psychological SciencesNational Health and Medical Research CouncilMedical Research CouncilNational Institute of Neurological Disorders and StrokeCenter for Neurotechnology, University of WashingtonWake Forest School of MedicineUniversity of North Carolina at Chapel HillSchool of Medicine, Emory UniversityWeill Cornell Medical CollegeBiogenSunnybrook Research InstituteSchool of Medicine, Duke UniversityNational Institutes of HealthUniversity of NicosiaSociedade Beneficente Israelita Brasileira Albert EinsteinImperial College LondonConselho Nacional de Desenvolvimento Científico e TecnológicoMinistero della SaluteUniversity of Southern CaliforniaWellcome TrustEberhard Karls Universität TübingenNational Institute of Child Health and Human DevelopmentTemple UniversityNational Heart, Lung, and Blood InstituteUniversity of South CarolinaTianjin Medical UniversityCanadian Institutes of Health ResearchUniversity of South DakotaDell Medical School, University of Texas at AustinUniversity of South AustraliaThomas Jefferson UniversityU.S. Department of Veterans AffairsUniversity of TorontoEmory UniversityMassachusetts General Hospital
KeywordsMedicineStroke (engine)Physical medicine and rehabilitationCognitive impairmentHippocampal formationFunctional impairmentChronic strokeCardiologyInternal medicinePhysical therapyRehabilitationDisease
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.024
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 head0.042
GPT teacher head0.330
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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 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
Citations9
Published2022
Admission routes2
Has abstractyes