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Factors associated with frailty syndrome in elderly women

2017· article· en· W2767513792 on OpenAlexaboutno aff
Clóris Regina Blanski Grden, Vanessa Regina de Andrade, Luciane Patrícia Andreani Cabral, Péricles Martim Reche, Erildo Vicente Müller, Pollyanna Kássia de Oliveira Borges

Bibliographic record

VenueRev Rene · 2017
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsFrailty syndromeMedicineGerontologyFrailty Index

Abstract

fetched live from OpenAlex

Objective: to evaluate the factors associated with frailty syndrome in elderly women in an outpatient clinic. Methods: cross-sectional study with 252 elderly women. The Mini Mental State Examination and the Edmonton Frail Scale were applied. The association of variables was performed using simple linear regression (Fisher’s F and Student’s t tests), p≤0.05.Results: there was prevalence of married women (44.4%) with low schooling (50.0%) and living with relatives (50.8%). Among them, 28.6% had mild, 13.0% moderate and 3.6% severe frailty. The factors associated with the syndrome were age (p=0.021), level of education (p=0.001), living with relatives (p=0.013), illnesses (p=0.023), falls (p=0.001) and hospitalization in the last 12 months (p=0.001). Conclusion: evidenced that almost half of the sample presented some type of frailty. Thus, it is important to evaluate this population frequently, considering the associated factors that can contribute to 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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.303
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

Citations2
Published2017
Admission routes1
Has abstractyes

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