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Record W2940611233 · doi:10.1016/j.dadm.2019.02.007

The Meta VCI Map consortium for meta‐analyses on strategic lesion locations for vascular cognitive impairment using lesion‐symptom mapping: Design and multicenter pilot study

2019· article· en· W2940611233 on OpenAlexfundno aff
Nick A. Weaver, Lei Zhao, J. Matthijs Biesbroek, Hugo J. Kuijf, Hugo P. Aben, Hee‐Joon Bae, Miguel Ángel Araque Caballero, Francesca M. Chappell, Christopher Chen, Martin Dichgans, Marco Duering, Marios K. Georgakis, Ruben S. van der Giessen, Bibek Gyanwali, Olivia KL Hamilton, Saima Hilal, Elise M. vom Hofe, Paul L.M. de Kort, Peter J. Koudstaal, Bonnie Lam, Jae‐Sung Lim, Stephen Makin, Vincent Mok, Lin Shi, Maria C. Valdés Hernández, Narayanaswamy Venketasubramanian, Joanna M. Wardlaw, Frank A. Wollenweber, Adrian Wong, Xin Xu, Charles DeCarli, Elaine Fletcher, Pauline Maillard, Josephine L. Barnes, Carole H. Sudre, Jonathan M. Schott, M. Arfan Ikram, Janne M. Papma, Rebecca M. E. Steketee, Meike W. Vernooij, Régis Bordet, Renaud Lopes, Cheng‐Wei Huang, Richard Frayne, Cheryl R. McCreary, Eric E. Smith, Walter H. Backes, Sebastian Köhler, Robert J. van Oostenbrugge, Julie Staals, Frans Verhey, Jack C. Y. Cheng, Raj N. Kalaria, David J. Werring, Jung‐Lung Hsu, Kuo‐Lun Huang, Jeroen van der Grond, J. Wouter Jukema, Roos C. van der Mast, Tanja C.W. Nijboer, K.‐H. Yu, R. Schmidt, Lukas Pirpamer, Bradley J. MacIntosh, Andrew D. Robertson, F.‐E. de Leeuw, Anil M. Tuladhar, Nimisha Chaturvedi, Therese Tillin, Henry Brodaty, Perminder S. Sachdev, Frederik Barkhof, Wiesje M. van der Flier, L. Jaap Kappelle, Geert Jan Biessels

Bibliographic record

VenueAlzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeKaohsiung Chang Gung Memorial HospitalEngineering and Physical Sciences Research CouncilLeids Universitair Medisch CentrumMedical Research CouncilUniversity of California, DavisMedizinische Universität GrazFood and Health BureauKarl-Franzens-Universität GrazMaastricht Universitair Medisch CentrumStichting CoolsingelHallym UniversityUniversitair Medisch Centrum UtrechtZonMwUniversity of EdinburghNational Key Research and Development Program of ChinaUniversiteit LeidenTaipei Medical UniversityNewcastle UniversityNational Institute on AgingChang Gung Medical FoundationMenzies Centre for Australian Studies, King's College London, University of LondonUniversity of TorontoWellcome TrustUniversity College LondonKing's College LondonUniversité de LilleSunnybrook Research InstituteMrs Gladys Row Fogo Charitable Trust
KeywordsHyperintensityLesionCognitionMedicineStroke (engine)PopulationClinical trialMagnetic resonance imagingRadiologyPsychologyInternal medicinePathologyPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: The Meta VCI Map consortium performs meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping. Integration of data from different cohorts will increase sample sizes, to improve brain lesion coverage and support comprehensive lesion-symptom mapping studies. METHODS: Cohorts with available imaging on white matter hyperintensities or infarcts and cognitive testing were invited. We performed a pilot study to test the feasibility of multicenter data processing and analysis and determine the benefits to lesion coverage. RESULTS: Forty-seven groups have joined Meta VCI Map (stroke n = 7800 patients; memory clinic n = 4900; population-based n = 14,400). The pilot study (six ischemic stroke cohorts, n = 878) demonstrated feasibility of multicenter data integration (computed tomography/magnetic resonance imaging) and achieved marked improvement of lesion coverage. DISCUSSION: Meta VCI Map will provide new insights into the relevance of vascular lesion location for cognitive dysfunction. After the successful pilot study, further projects are being prepared. Other investigators are welcome to join.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.308
GPT teacher head0.427
Teacher spread0.118 · 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.

Study designMeta-analysis
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

Citations48
Published2019
Admission routes1
Has abstractyes

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