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Record W2018128605 · doi:10.1016/j.jalz.2009.05.623

IC‐P‐069: Cerebral ventricular subvolume analysis from MRI: A marker of Alzheimer's disease progression validated using ADNI

2009· article· en· W2018128605 on OpenAlexaff
Sean M. Nestor, Michael Borrie, Matthew Smith, Robert Bartha

Bibliographic record

VenueAlzheimer s & Dementia · 2009
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsLawson Health Research InstituteSt Joseph's Health CareWestern University
Fundersnot available
KeywordsVentricleMagnetic resonance imagingLateral ventriclesCognitive impairmentMedicineCardiologyNeuroimagingInternal medicineNuclear medicineAlzheimer's diseaseAlzheimer's Disease Neuroimaging InitiativeDiseasePathologyRadiology

Abstract

fetched live from OpenAlex

Cerebral ventricular volume measured from magnetic resonance images (MRI) is an indirect global marker of Alzheimer disease (AD) progression. Quantification of ventricular sub-regions may allow characterization of topographical disease progression and may also detect regional differences between normal elderly controls (NEC), subjects with mild cognitive impairment (MCI) and AD in vivo. Objectives: To evaluate ventricle sub-region volume and enlargement between NEC, MCI, and AD groups. Baseline and 12-month clinical and MRI data were obtained for 511 subjects (NEC = 106;MCI = 261;AD = 124) from the Alzheimer Disease Neuroimaging Initiative database. Ventricular volume sub-region analysis was performed using Brain Ventricle Quantification (BVQ) (Cedara Software). BVQ parcellates the ventricles into 10 anatomical regions using highly reproducible anatomical rules. One-way ANOVA and Student-Newman-Keuls tests were used for group-wise comparisons. All volumes are reported as mean ± SD. Baseline total (NEC = 1.5 ± 1.0 cm; MCI = 2.2 ± 1.5 cm; AD = 3.0 ± 2.0 cm) and bilateral temporal horn volumes were significantly different between all three groups (p < 0.01). Total anterior horn volumes were greater for both the MCI (12.6 ± 7.6 cm) and AD groups (13.8 ± 8.4 cm) in comparison to NEC (11.0 ± 6.8 cm)(p < 0.01). The AD group had a significantly greater total mid-body volume (14.9 ± 7.0 cm) than the MCI (13.4 ± 6.3 cm) and NEC groups (12.3 ± 5.7 cm)(p < 0.01). All groups had significantly different total posterior horn volumes (NEC = 16.4 ± 9.6 cm; MCI = 20.5 ± 12.7 cm; AD = 24.3 ± 15.0 cm)(p < 0.01). Bilateral temporal horn enlargement was significantly greater for the AD group (0.3 ± 0.3 cm) than the MCI (0.2 ± 0.2 cm) and NEC groups (0.1 ± 0.2 cm)(p < 0.01). Subjects with MCI had a greater average right temporal horn enlargement than NEC subjects (p < 0.01). Baseline volume and enlargement within the bilateral posterior cornus and temporal horns suggests marked atrophy occurs around the medial temporal lobe in MCI, and to a greater extent in AD in comparison to NEC. Persons with MCI had larger anterior horns than NEC, suggesting frontal lobe atrophy may occur in MCI. Cross-sectional and longitudinal measures of ventricular sub-regions reflect the topographical progression of AD in vivo.

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)0.002
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.026
GPT teacher head0.326
Teacher spread0.300 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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