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Record W7120843810

Relationship between cognition, depressive symptoms and fragility in the elderly

2023· dissertation· pt· W7120843810 on OpenAlexaboutno aff
Suélly Krein Heuert

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2023
Typedissertation
Languagept
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsGeriatricsDepression (economics)CognitionOutpatient clinicGeriatric Depression ScalePopulationDepressive symptomsObservational studyAssociation (psychology)
DOInot available

Abstract

fetched live from OpenAlex

In recent years, the population has been experiencing a marked aging process. Understanding the various characteristics of the aging process helps in caring for the elderly population. The functionality of the elderly is directly affected by frailty, which, in turn, is defined as increased vulnerability. Studies show that cognitive disability and physical frailty have a significant association between them. Therefore, this research aims to evaluate the cognitive factors and depressive symptoms associated with frailty and loss of autonomy in elderly people treated at a geriatrics outpatient clinic of a highly complex tertiary care hospital. The study is characterized as a cross-sectional observational study with a quantitative and qualitative approach. The research is part of a larger project entitled “Risk factors for the development of frailty in the elderly” and has CEP opinion number 5,572,399. Statistical analysis of the data was performed using the SPSS Program (Statistics Package for the Social Sciences) version 22.0. Clinical and sociodemographic data are described by absolute (N) and relative (%) frequency. Tests were also performed for analysis of trends and association between categorical variables using Pearson's chi-square test. Tests were performed to verify associations between scores on the frailty scale, related to the cognition and depression scales. For all analyses, p<0.05 was used as a reference. The study sample consisted of medical records of 36 elderly patients treated at the Geriatrics outpatient clinic. In view of the sociodemographic profile of the population studied, the predominance of males should be highlighted, with the majority aged between 70 and 79 years, with low education, most with incomplete primary school, followed by illiterates. The sample, for the most part, did not have cognitive complaints and did not use drugs related to dementia. As for the use of medication for depressive symptoms and other mood disorders, most of the sample used them. There was a predominance of elderly people considered pre-frail, followed by frail. When analyzing the cognitive scales, most of the sample presented scores below the expected scores. It was also found that there is an association between the Frail Scale and the Semantic Verbal Fluency Test, the Clock Drawing Test and the Brief Battery of Cognitive Screening, in the areas of Immediate Memory and Late Memory. It is worth mentioning that there was no association between the Frail scale and the presence of depressive symptoms, assessed by the Geriatic Depression Scale - 15. An integrative review was also carried out in order to identify the cognitive assessments most used during the diagnosis of cognitive frailty, which include: Mini Mental State Examination, Digit Span and Montreal Cognitive Assessment. The results presented brought important data and associations regarding frailty and cognitive impairment in the elderly. It is hoped that these data will contribute to the development of scales that address cognitive frailty and specific care for the elderly population, in order to direct, improve and qualify the care offered by health professionals, aiming at aging with autonomy, independence and quality of life.

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)0.002
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.035
GPT teacher head0.288
Teacher spread0.253 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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