MétaCan
Menu
← Back to cohort
Record W2043073239 · doi:10.1016/j.jalz.2014.05.1756

The EADC‐ADNI Harmonized Protocol for manual hippocampal segmentation on magnetic resonance: Evidence of validity

2014· article· en· W2043073239 on OpenAlexafffund
Giovanni B. Frisoni, Clifford R. Jack, Martina Bocchetta, Corinna M. Bauer, Kristian Steen Frederiksen, Yawu Liu, Gregory M. Preboske, Tim Swihart, Melanie Blair, Enrica Cavedo, Michel J. Grothe, Mariangela Lanfredi, Oliver Martinez, Masami Nishikawa, Marileen Portegies, Travis Stoub, Chadwich Ward, Liana G. Apostolova, Rossana Ganzola, Dominik Wolf‎, Frederik Barkhof, George Bartzokis, Charles DeCarli, John G. Csernansky, Leyla deToledo‐Morrell, Mirjam I. Geerlings, Jeffrey Kaye, Ronald Killiany, Stéphane Lehéricy, Hiroshi Matsuda, John T. O’Brien, Lisa C. Silbert, Philip Scheltens, Hilkka Soininen, Stefan Teipel, Gunhild Waldemar, Andreas Fellgiebel, Josephine Barnes, Michael Firbank, Lotte Gerritsen, Wouter J.P. Henneman, Nikolai Malykhin, Jens C. Pruessner, Lei Wang, Craig Watson, Henrike Wolf, Mony J. de Leon, Johannes Pantel, Clarissa Ferrari, Paolo Bosco, Patrizio Pasqualetti, Simon Duchesne, Henri M. Duvernoy, Marina Boccardi, Marilyn S. Albert, David Bennet, Richard Camicioli, D. Louis Collins, Bruno Dubois, Harald Hampel, Tom denHeijer, Christofer Hock, William J. Jagust, Leonore J. Launer, Jerome J. Maller, S. G. Mueller, Perminder S. Sachdev, Andy Simmons, Paul M. Thompson, Peter‐Jelle Visser, Lars‐Olof Wahlund, Michael W. Weiner, Bengt Winblad

Bibliographic record

VenueAlzheimer s & Dementia · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of AlbertaUniversité LavalMcGill UniversityInstitut Universitaire en Santé Mentale de Québec
FundersNational Institute on AgingUniversity of California, San FranciscoUniversity of California, San DiegoNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchUniversity of California, Los AngelesNational Institutes of HealthInnogeneticsServierKuopion Yliopistollinen SairaalaEisaiAgence Nationale de la RechercheBayer HealthCareKing's College LondonNorthern California Institute for Research and EducationRush UniversityJohns Hopkins UniversityDeutsches Zentrum für Neurodegenerative ErkrankungenUniversité LavalPfizerNorthwestern UniversityBioClinicaUniversity of AlbertaSchool of Medicine, Boston UniversityGE HealthcareAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsSynarcMcGill UniversityRocheUniversity of Southern CaliforniaAbbott FundBristol-Myers SquibbEli Lilly and CompanyBrain Research TrustAstraZenecaNovartis Pharmaceuticals CorporationTakeda Pharmaceutical CompanyMedpaceBiogen IdecAlzheimer's AssociationAmorfix Life SciencesKarolinska InstitutetAlzheimer's Drug Discovery FoundationMerck
KeywordsHARPSegmentationProtocol (science)NeuroimagingNuclear medicineMagnetic resonance imagingDiffusion MRIMedicinePsychologyComputer scienceArtificial intelligenceRadiologyPathologyPhysicsPsychiatry

Abstract

fetched live from OpenAlex

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 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)0.374
metaresearch head score (Gemma)0.379
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.772

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3740.379
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0030.006
Scholarly communication0.0030.002
Open science0.0040.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.095
GPT teacher head0.402
Teacher spread0.307 · 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 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

Citations206
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueAlzheimer s & Dementia→Same topicDementia and Cognitive Impairment Research→French-language works237,207→