MétaCan
Menu
Back to cohort
Record W150243577 · doi:10.3233/jad-2005-8106

Asynchronous regional brain volume losses in presymptomatic to moderate AD

2005· article· en· W150243577 on OpenAlexaff
Jeffrey Kaye, M. M. Moore, A. Dame, Joseph F. Quinn, Richard Camicioli, Diane Howieson, E. Corbridge, B. Care, Gary M. Nesbit, Gary Sexton

Bibliographic record

VenueJournal of Alzheimer s Disease · 2005
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Alberta
FundersNational Center for Complementary and Integrative HealthNational Center for Research ResourcesNational Institute on Aging
KeywordsClinical Dementia RatingDementiaTemporal lobeMedicineBrain sizeInternal medicineCardiologyProspective cohort studyNuclear medicineDiseaseMagnetic resonance imagingEpilepsyRadiologyPsychiatry

Abstract

fetched live from OpenAlex

To determine if rates and locations of brain volume loss associated with AD are phase-specific, occurring prior to clinical onset and at later stages, we performed longitudinal volumetric MRI analysis on 155 subjects enrolled in a prospective study of aging and dementia. Subjects were divided by Clinical Dementia Rating (CDR) scale into stages of Normal (CDR 0 --> 0), Very Mild (CDR 0 --> 0.5 and 0.5 --> 0.5), Mild (CDR 0.5 --> 1.0 and 1.0 --> 1.0) and Moderate (CDR 1.0 --> 2.0 and 2.0 --> 2.0) dementia. Rates of volume change in CSF spaces, lobar and medial temporal lobe regions were analyzed for group differences across stages. Annual rates of ventricular volume change differed between non-demented and very mild group (p<0.01). In later severity stages, ventricular, temporal, basal ganglia-thalamic region and total volumes show change. Rates of volume loss increase as dementia progresses, but not uniformly in all regions. These regional and phase-specific volume changes form targets for monitoring disease-modifying therapies at clinically relevant, defined stages of dementia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.329
Teacher spread0.304 · 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.

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

Citations33
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Alzheimer s DiseaseSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207