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Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

2015· article· en· W2135011268 on OpenAlexafffund
Esther E. Bron, Marion Smits, Wiesje M. van der Flier, Hugo Vrenken, Frederik Barkhof, Philip Scheltens, Janne M. Papma, Rebecca M. E. Steketee, Carolina Méndez‐Orellana, Rozanna Meijboom, Madalena Pinto, Joana R. Meireles, Carolina Garrett, António J. Bastos‐Leite, Ahmed Abdulkadir, Olaf Ronneberger, Nicola Amoroso, R. Bellotti, David Cárdenas‐Peña, Andrés Marino Álvarez-Meza, Chester Dolph, Khan M. Iftekharuddin, Simon Fristed Eskildsen, Pierrick Coupé, Vladimir Fonov, Katja Franke, Christian Gaser, Christian Ledig, Ricardo Guerrero, Tong Tong, Katherine R. Gray, Elaheh Moradi, Jussi Tohka, Alexandre Routier, Stanley Durrleman, Alessia Sarica, Giuseppe Di Fatta, F. Sensi, A. Chincarini, G. M. Smith, Zhivko V. Stoyanov, Lauge Sørensen, Mads Nielsen, Sabina Tangaro, Paolo Inglese, Christian Wachinger, Martin Reuter, John C. van Swieten, Wiro J. Niessen, Stefan Klein

Bibliographic record

VenueNeuroImage · 2015
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMontreal Neurological Institute and HospitalMcGill University
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchUniversity of California, San DiegoNational Institutes of HealthIXICOServierStichting DioraphteEisaiGenentechErasmus Medisch CentrumVrije Universiteit AmsterdamRadboud UniversiteitNorthern California Institute for Research and EducationAlzheimer NederlandPfizerBiogenBioClinicaRadboud Universitair Medisch CentrumF. Hoffmann-La RocheSynarcUniversity of Southern CaliforniaMedpaceNovartis Pharmaceuticals CorporationU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsAlzheimer's AssociationNational Institute on AgingCommonwealth Scientific and Industrial Research OrganisationVanderbilt University Medical CenterFoundation for the National Institutes of Health
KeywordsDementiaComputer scienceAlgorithmArtificial intelligenceMachine learningMedical physicsPsychologyMedicinePathology

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)0.051
metaresearch head score (Gemma)0.137
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.137
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0060.002
Science and technology studies0.0010.001
Scholarly communication0.0070.003
Open science0.0060.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.156
GPT teacher head0.349
Teacher spread0.193 · 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.

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

Citations352
Published2015
Admission routes2
Has abstractno

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