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Record W2050943144 · doi:10.1177/070674370404900203

Alzheimer's Disease, Genes, and Environment: The Value of International Studies

2004· article· en· W2050943144 on OpenAlexvenueno aff
Hugh C. Hendrie, Kathleen Hall, Adesola Ogunniyi, Sujuan Gao

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2004
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsYorubaDementiaDiseaseGerontologyPopulationRisk factorIncidence (geometry)Ethnic groupAlzheimer's diseaseMedicineDemographyEnvironmental healthPathologySociology

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the construction of a disease model incorporating both genetic an environmental factors in the etiology of Alzheimer's disease (AD), using data generated from the Indianapolis-Ibadan dementia project (I-IDP). METHOD: The I-IDP is a longitudinal comparative study of the prevalence and incidence o dementia in 2 communities: elderly African Americans living in Indianapolis, Indiana, an Yoruba living in Ibadan, Nigeria. RESULTS: African Americans are more than twice as likely as Yoruba to develop AD. Possible explanations for this finding include genetic factors: the possession of the apolipoprotein E epsilon4 allele does not increase risk for AD among Yoruba but confers a sligh increase in AD risk for African Americans. As well, environmental factors may play a role: African Americans have a higher risk of vascular risk factors than do Yoruba. CONCLUSIONS: International comparative studies, particularly those involving population from developing and developed countries, offer a unique opportunity for applying new in formation regarding population genetics to traditional AD risk factor 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 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)0.020
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.006
Scholarly communication0.0050.009
Open science0.0010.003
Research integrity0.0010.002
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 head0.023
GPT teacher head0.298
Teacher spread0.275 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations38
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Psychiatry→Same topicDementia and Cognitive Impairment Research→French-language works237,207→