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Função cognitiva e controle da pressão arterial em idosos hipertensos

2022· article· pt· W4281836228 on OpenAlexaboutno aff
Alyne Leal de Alencar Luz, Aline Silva‐Costa, Elizabeth Leite Barbosa, Larissa Pruner Marques, Ester Paiva Souto, Rosane Härter Griep

Bibliographic record

VenueCiência & Saúde Coletiva · 2022
Typearticle
Languagept
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentBlood pressureMedicinePoisson regressionCognitionCognitive impairmentGerontologyCognitive Assessment SystemInternal medicinePsychiatryEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

This article aims to estimate the prevalence of cognitive impairment and analyze its association with blood pressure control in elderly hypertensive individuals. Cross-sectional study of 383 elderly hypertensive individuals in the state of Piauí, Brazil. The authors collected sociodemographic and clinical data, performed blood pressure measurement, and assessed cognitive function using the Montreal Cognitive Assessment (MoCA) test. Poisson regression with robust variance was used. Overall prevalence of cognitive impairment was 74.4%, higher in the age group 80 years and over and among older persons with less schooling. Prevalence of uncontrolled blood pressure was 61.6%, with a higher proportion in the elderly with cognitive impairment. An association was observed between cognitive impairment and uncontrolled blood pressure (aPR: 3.98; 95%CI = 2.51-6.33). The significant association between cognitive function and blood pressure control suggest that cognitive impairment is an important risk factor for uncontrolled blood pressure in older persons. The inclusion of screening measures for possible cognitive deficits may be useful for better monitoring blood pressure levels among elderly hypertensive individuals.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.264
Teacher spread0.231 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations6
Published2022
Admission routes1
Has abstractyes

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