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The EADC-ADNI harmonized protocol for hippocampal segmentation: A validation study

2018· article· en· W2810185413 on OpenAlexafffund
Azar Zandifar, Vladimir Fonov, Jens C. Pruessner, D. Louis Collins

Bibliographic record

VenueNeuroImage · 2018
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute on AgingFonds de recherche du Québec – Nature et technologiesNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchGenentechNational Institutes of HealthH. Lundbeck A/SServierEisaiCanada Foundation for InnovationU.S. Department of DefenseEli Lilly and CompanyCompute CanadaLundbeckfondenMinistère de l'Économie, de la Science et de l'Innovation - QuébecNorthern California Institute for Research and EducationMcGill UniversityAlzheimer's AssociationFujirebio USDoD Alzheimer's Disease Neuroimaging InitiativeNatural Sciences and Engineering Research Council of CanadaPfizerBiogenBioClinicaUniversity of Southern CaliforniaNovartis Pharmaceuticals CorporationBristol-Myers SquibbF. Hoffmann-La RocheMerckAlzheimer's Drug Discovery FoundationIXICOTakeda Pharmaceutical CompanyAbbVieGE HealthcareAlzheimer's Disease Neuroimaging InitiativeJohnson and JohnsonMeso Scale Diagnostics
KeywordsHARPSegmentationHippocampal formationComputer scienceArtificial intelligencePattern recognition (psychology)PsychologyNeuroscience

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)0.040
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.054
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0020.002
Open science0.0040.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.098
GPT teacher head0.429
Teacher spread0.331 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations10
Published2018
Admission routes2
Has abstractno

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