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Record W2126498806 · doi:10.1017/s1041610202008116

Estimating the Prevalence of Dementia in Elderly People: A Comparison of the Canadian Study of Health and Aging and National Population Health Survey Approaches

2001· article· en· W2126498806 on OpenAlexaffabout
Vince Salazar Thomas, Sultan Darvesh, Chris MacKnight, Kenneth Rockwood

Bibliographic record

VenueInternational Psychogeriatrics · 2001
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDementiaProxy (statistics)Population healthMedicineGerontologyHealth economicsPublic healthPopulationCognitionCommunity healthEnvironmental healthPsychiatryStatisticsNursingPathology

Abstract

fetched live from OpenAlex

The Canadian Study of Health and Aging (CSHA) and the National Population Health Survey (NPHS) collected data on the prevalence of dementia in differing fashions. The CSHA used a two-stage method with objective testing and expert judgment, and the NPHS used self-report and proxy data. The present report compares estimates of prevalence and the methodology for ascertainment in the two surveys. The more detailed approach of the CSHA offers the more valid means of estimating prevalence and providing data on subtypes, and can be used in naturalhistory studies. TheNPHSmeasures, including a self/proxy report of diagnosed dementia and a derived cognitive measure, are not sufficiently valid for useful inferences to be made. However, the NPHS method can be improved through supplementation with data on functional disability, providing age group-specific point estimates closer to the CSHA's estimates of cognitive impairment and dementia from the community sample. Future waves of the NPHS may wish to include objective cognitive function measures as a cost-efficient and more accurate method of estimating the prevalence of the dementia syndrome without attempting to estimate the prevalence of particular causes of that syndrome.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.091
GPT teacher head0.411
Teacher spread0.320 · 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

Citations41
Published2001
Admission routes2
Has abstractyes

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