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Record W2122058578 · doi:10.3233/jad-2010-100201

Effect of APOE ε4 Allele on Cortical Thicknesses and Volumes: The AddNeuroMed Study

2010· article· en· W2122058578 on OpenAlexaff
Yawu Liu, Teemu Paajanen, Eric Westman, Lars‐Olof Wahlund, Andrew Simmons, Catherine Tunnard, Tomasz Sobów, Petroula Proitsi, John Powell, Patrizia Mecocci, Magda Tsolaki, Bruno Vellas, Sebastian Muehlboeck, Alan C. Evans, Christian Spenger, Simon Lovestone, Hilkka Soininen

Bibliographic record

VenueJournal of Alzheimer s Disease · 2010
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsHippocampusAmygdalaApolipoprotein EInternal medicineOrbitofrontal cortexMedicineGyrusCaudate nucleusEndocrinologyPsychologyNeurosciencePrefrontal cortexDiseaseCognition

Abstract

fetched live from OpenAlex

The apolipoprotein E (APOE) ε4 allele is a risk factor for Alzheimer's disease (AD), but its effect on brain volumes is controversial. We explored the effect of the ε4 allele on regional cortical thickness and volume measurements using an automated pipeline in 111 subjects with mild cognitive impairment (MCI), 115 AD patients, and 107 age-matched healthy controls. The clinical data were used as covariates in the thickness and volume comparisons. The ε4 carriers had significantly smaller volume than non-carriers in caudate (p=0.028) in controls; in amygdala and caudate in the MCI group (p <or= 0.049); and in hippocampus and amygdala in the AD group (p <or= 0.001). In the female subjects, the ε4 carriers had significantly thinner cortical thickness or smaller volume than non-carriers in medial orbitofrontal gyrus and caudate in controls (p <or= 0.014); in amygdala in MCI subjects (p=0.047) and in hippocampus and amygdala in AD patients (p

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.338
Teacher spread0.319 · 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

Citations92
Published2010
Admission routes1
Has abstractyes

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