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Record W3127837226 · doi:10.1002/dad2.12164

Genetic risk for Alzheimer's disease, cognition, and mild behavioral impairment in healthy older adults

2021· article· en· W3127837226 on OpenAlexaff
Byron Creese, Ryan Arathimos, Helen Brooker, Dag Aarsland, Anne Corbett, Cathryn M. Lewis, Clive Ballard, Zahinoor Ismail

Bibliographic record

VenueAlzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Calgary
FundersMedical Research CouncilKing's College LondonDepartment of Health and Social CareMenzies Centre for Australian Studies, King's College London, University of LondonUniversity of ExeterNational Institute for Health and Care ResearchMaudsley Charity
KeywordsDementiaCognitionChecklistDiseaseAssociation (psychology)Cognitive impairmentConfidence intervalClinical psychologyPsychologyAlzheimer's diseaseMedicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background The neuropsychiatric syndrome mild behavioral impairment (MBI) describes an at‐risk state for dementia and may be a useful screening tool for sample enrichment. We hypothesized that stratifying a cognitively normal sample on MBI status would enhance the association between genetic risk for Alzheimer's disease (AD) and cognition. Methods Data from 4458 participants over age 50 without dementia was analyzed. A cognitive composite score was constructed and the MBI Checklist was used to stratify those with MBI and those without. Polygenic scores for AD were generated using summary statistics from the IGAP study. Results AD genetic risk was associated with worse cognition in the MBI group but not in the no MBI group (MBI: β = –0.09, 95% confidence interval: –0.13 to –0.03, P = 0.002, R 2 = 0.003). The strongest association was in those with more severe MBI aged ≥65. Conclusions MBI is an important feature of aging; screening on MBI may be a useful sample enrichment strategy for clinical research.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.033
GPT teacher head0.370
Teacher spread0.337 · 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.

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

Citations74
Published2021
Admission routes1
Has abstractyes

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