MétaCan
Menu
Back to cohort
Record W2048614656 · doi:10.1001/archneurol.2009.312

Hypertension, Executive Dysfunction, and Progression to Dementia

2010· article· en· W2048614656 on OpenAlexaffabout
Shahram Oveisgharan, Vladimir Hachinski

Bibliographic record

VenueArchives of Neurology · 2010
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsWestern University
Fundersnot available
KeywordsDementiaExecutive dysfunctionMedicineCognitionCognitive declineInternal medicineCardiologyPsychiatryDiseaseNeuropsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Midlife hypertension has long been established as a risk factor for dementia, but the role of late-life hypertension remains unclear. OBJECTIVE: To investigate the role of hypertension in cognitive deterioration among older subjects with cognitive impairment, no dementia. DESIGN: The Canadian Study of Health and Aging was conducted in 3 waves (1991, 1995-1996, and 2001-2002). SETTING: Community-based cohort study. PATIENTS: We studied 990 subjects with a mean (SD) age of 83.06 (6.97) years having cognitive impairment, no dementia who were followed up for 5 years in the Canadian Study of Health and Aging. MAIN OUTCOME MEASURES: Determination of cognitive dysfunction and association between hypertension and cognitive deterioration. RESULTS: No difference in the rate of progression to dementia based on the presence of hypertension was found between subjects with memory dysfunction alone or in combination with executive dysfunction. However, among subjects with executive dysfunction alone, 57.7% having hypertension progressed to dementia compared with 28.0% having normotension (P = .02). CONCLUSIONS: Hypertension predicts progression to dementia in older subjects with executive dysfunction but not memory dysfunction. Control of hypertension could prevent progression to dementia in one-third of the subjects with cognitive impairment, no 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.252

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)0.0000.000

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 head0.011
GPT teacher head0.285
Teacher spread0.274 · 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

Citations85
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueArchives of NeurologySame topicDementia and Cognitive Impairment ResearchFrench-language works237,207