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Record W2902211786 · doi:10.1177/1533317518813550

Prevalence and Causes of Cognitive Impairment and Dementia in a Population-Based Cohort From Northern Portugal

2018· article· en· W2902211786 on OpenAlexaboutno aff
Luís Ruano, Natália Araújo, Mariana Branco, Rui Barreto, Sandra Moreira, Ricardo Pais, Vítor Tedim Cruz, Nuno Lunet, Henrique Barros

Bibliographic record

VenueAmerican Journal of Alzheimer s Disease & Other Dementias® · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaUniversidade do Porto
KeywordsDementiaEpidemiologyVascular dementiaCohortMedicinePopulationCohort studyStroke (engine)GerontologyDiseaseEtiologyIncidence (geometry)PediatricsPsychiatryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Vascular disease may play an important role in the epidemiology of dementia in countries with high stroke incidence, such as Portugal. OBJECTIVE: To assess the prevalence and etiology of cognitive impairment in a population-based cohort from Portugal. METHODS: Individuals ≥55 years (n = 730) from the EPIPorto cohort were assessed using the Mini-Mental State Examination and the Montreal Cognitive Assessment. Those scoring below the age-/education-adjusted cutoff points were further evaluated to identify dementia or mild cognitive impairment (MCI) and to define its most common causes. RESULTS: Thirty-six cases of MCI/dementia were identified, corresponding to adjusted prevalences of 4.1% for MCI and 1.3% for dementia. The most common cause of MCI/dementia was vascular (52.8%), followed by Alzheimer's disease (36.1%). CONCLUSION: These findings highlight the importance of vascular cognitive impairment in the epidemiology of dementia in Portugal and carry an important public health message regarding its prevention and management, possibly extending to other countries with a high-stroke burden.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.705

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.301
Teacher spread0.287 · 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

Citations30
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Alzheimer s Disease & Other Dementias®Same topicDementia and Cognitive Impairment ResearchFrench-language works237,207