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Worsening frailty in community-dwelling older people with hypertension and associated factors

2022· article· en· W4362713464 on OpenAlexaboutno aff
Marianne Silva Soares, Gabriela Alves de Brito, Isamara Corrêa Guimarães Horta, Isabella Ribeiro Gomes, Jair Almeida Carneiro, Fernanda Marques da Costa

Bibliographic record

VenueRevista Brasileira de Geriatria e Gerontologia · 2022
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsPolypharmacyGerontologyOlder peopleMedicinePoisson regressionSocioeconomic statusDemographyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Objective To estimate the prevalence and factors associated with the worsening of frailty in older people with arterial hypertension. Methods Quantitative, longitudinal, prospective and analytical study. Carried out in community-dwelling hypertensive older people from Minas Gerais. Sampling was probabilistic, by clusters in two stages. Data collection took place at the older people's homes in two moments. Demographic, socioeconomic and clinical-assistance variables were analyzed. Frailty was measured by the Edmonton Frailty Scale. Poisson regression with robust variance was used to obtain crude and adjusted prevalence ratios. Results 281 older people participated in the study, 23.1% showed a worsening of their state of frailty. The prevalence of frailty increased from 38.0% in the base year to 31.2% in the first wave. The worsening of frailty was associated with negative self-perception of health, polypharmacy and hospitalization in the last 12 months. Conclusion There was a transition between states of frailty. An important contingent of the older people showed worsening frailty.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.280
Teacher spread0.240 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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