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Record W4392894967 · doi:10.18621/eurj.1376545

Association of frailty with nutritional parameters in patients with chronic kidney disease

2024· article· en· W4392894967 on OpenAlexaboutno aff
Recep Evcen, Mehmet Zahid Koçak, Rengin Elsürer Afşar

Bibliographic record

VenueThe European Research Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMalnutritionKidney diseaseFrailty syndromeRenal functionInternal medicineFrailty Index

Abstract

fetched live from OpenAlex

Objectives: Frailty is a significant clinical syndrome characterized by greater susceptibility to stressors due to the dysfunction of multiple organ systems, which increases in prevalence with age. This study was performed to investigate relations between frailty and nutritional parameters in patients with chronic kidney disease (CKD). Methods: This cross-sectional study involved 100 CKD patients aged 50 years or older. Frailty was assessed using the Edmonton Frailty Scale (EFS) and Fried’s Frailty Scale (FFS). The patients nutritional status was assessed using the Mini Nutritional Assessment (MNA) and the routine laboratory tests. Results: The study included 100 patients, consisting of 41 females and 59 males. The mean age of the participants was 65.3±9.3 years. The median glomerular filtration rate (GFR) of the patients was 23 mL/min/1.73 m2) (min: 3-max: 65). According to the MNA, 15 patients had normal nutritional status, 63 were at risk of malnutrition, and 22 were malnourished. According to the EFS score, four patients were categorized as not frail, 11 as vulnerable, 25 with mild frailty, 21 with moderate frailty, and 39 with severe frailty. According to the FFS score, six patients were non-frail, 30 were classified as pre-frail, and 64 were considered frail. Conclusions: Frailty and malnutrition in patients with CKD were independently related to all other factors examined in this study. Screening for malnutrition at the early stages in patients with CKD and the appropriate treatment may prevent the development of 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 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)0.003
metaresearch head score (Gemma)0.001
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 score0.177
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.324
Teacher spread0.285 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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