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Record W2111844615 · doi:10.1016/j.jalz.2011.04.007

Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer's disease

2011· article· en· W2111844615 on OpenAlexaff
Clifford R. Jack, Frederik Barkhof, Matt A. Bernstein, Marc Cantillon, Patricia E. Cole, Charles DeCarli, Bruno Dubois, Simon Duchesne, Nick C. Fox, Giovanni B. Frisoni, Harald Hampel, David Hill, Keith A. Johnson, Jean‐François Mangin, Philip Scheltens, Adam J. Schwarz, Reisa A. Sperling, Joyce Suhy, Paul M. Thompson, Michael W. Weiner, Norman L. Foster

Bibliographic record

VenueAlzheimer s & Dementia · 2011
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité Laval
FundersNational Institute on AgingAlzheimer's AssociationMedical Research CouncilEngineering and Physical Sciences Research CouncilNational Institute for Health and Care ResearchNational Institutes of HealthPfizer
KeywordsStandardizationNeuroimagingAlzheimer's Disease Neuroimaging InitiativeClinical trialCredentialingMedical physicsDiseaseMedicineAlzheimer's diseaseMagnetic resonance imagingComputer sciencePathologyRadiologyPsychiatryMedical education

Abstract

fetched live from OpenAlex

BACKGROUND: The promise of Alzheimer's disease biomarkers has led to their incorporation in new diagnostic criteria and in therapeutic trials; however, significant barriers exist to widespread use. Chief among these is the lack of internationally accepted standards for quantitative metrics. Hippocampal volumetry is the most widely studied quantitative magnetic resonance imaging measure in Alzheimer's disease and thus represents the most rational target for an initial effort at standardization. METHODS AND RESULTS: The authors of this position paper propose a path toward this goal. The steps include the following: (1) Establish and empower an oversight board to manage and assess the effort, (2) adopt the standardized definition of anatomic hippocampal boundaries on magnetic resonance imaging arising from the European Alzheimer's Disease Centers-Alzheimer's Disease Neuroimaging Initiative hippocampal harmonization effort as a reference standard, (3) establish a scientifically appropriate, publicly available reference standard data set based on manual delineation of the hippocampus in an appropriate sample of subjects (Alzheimer's Disease Neuroimaging Initiative), and (4) define minimum technical and prognostic performance metrics for validation of new measurement techniques using the reference standard data set as a benchmark. CONCLUSIONS: Although manual delineation of the hippocampus is the best available reference standard, practical application of hippocampal volumetry will require automated methods. Our intent was to establish a mechanism for credentialing automated software applications to achieve internationally recognized accuracy and prognostic performance standards that lead to the systematic evaluation and then widespread acceptance and use of hippocampal volumetry. The standardization and assay validation process outlined for hippocampal volumetry was envisioned as a template that could be applied to other imaging biomarkers.

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 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)0.785
metaresearch head score (Gemma)0.730
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.785
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7850.730
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0130.008
Science and technology studies0.0070.013
Scholarly communication0.0210.013
Open science0.0160.018
Research integrity0.0140.027
Insufficient payload (model declined to judge)0.0030.004

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 head0.148
GPT teacher head0.436
Teacher spread0.288 · 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 designNot applicable
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

Citations219
Published2011
Admission routes1
Has abstractyes

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