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Frailty and cognitive performance of elderly in the context of social vulnerability

2018· article· en· W2810201607 on OpenAlexaboutno aff
Isabela Thaís Machado de Jesus, Fabiana de Sousa Orlando, Marisa Silvana Zazzetta

Bibliographic record

VenueDementia & Neuropsychologia · 2018
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsGerontologySocial vulnerabilityContext (archaeology)Socioeconomic statusVulnerability (computing)CognitionLogistic regressionPsychologyMedicinePopulationEnvironmental healthPsychiatryPsychological interventionGeography

Abstract

fetched live from OpenAlex

Elderly who live in the context of social vulnerability have lower education and socioeconomic status. OBJECTIVE: To analyze cognitive performance as a factor associated with frailty status in elderly living in contexts of social vulnerability. METHODS: An exploratory, comparative, cross-sectional study using a quantitative method was conducted with elderly people registered at Social Assistance Reference Centers. A semi-structured interview, the Edmonton Frail Scale and Montreal Cognitive Assessment were applied. The project was approved by the Research Ethics Committee. To analyze the data, a logistic regression was performed considering two groups (frail and non-frail). RESULTS: 247 elderly individuals participated in the study, with a mean age of 68.52 (±SD =7.28) years and education of 1-4 years (n=133). All the elderly evaluated resided in vulnerable regions. Regarding frailty, 91 (36.8%) showed frailty at some level (mild, moderate or severe) and 216 (87.4%) had cognitive impairment. On the regression analysis, frailty was associated with number of diseases (OR:1.60; 95%CI: 1.28-1.99) and cognition (OR:0.93; 95%CI: 0.89-0.98). CONCLUSION: Identifying level of frailty and cognition in socially vulnerable elderly reinforces the need for early detection in both these conditions by the public services that provide care for this population with a focus on prevention.

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 score0.137
Threshold uncertainty score0.333

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)0.0000.000

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 head0.036
GPT teacher head0.329
Teacher spread0.294 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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