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Record W7131980046

Frailty syndrome in dialysis patients: one dialysis center study

2024· article· en· W7131980046 on OpenAlexaboutno aff
Irmante Bagdziuniene, Gintarė Butenytė, Edita Žiginskienė

Bibliographic record

VenueLithuanian University of Health Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsHemodialysisDialysisMortality rateFrailty syndromeRetrospective cohort studyComorbidityKidney disease
DOInot available

Abstract

fetched live from OpenAlex

Aim of the study. To analyze the rate of frailty syndrome among chronic hemodialysis (HD) patients (pts.) and it's association with outcomes. Objectives: 1. To evaluate the rate of frailty in chronic HD pts. 2. To assess relation of frailty to hospitalisation and mortality in chronic HD pts. Methods. A retrospective analysis of clinical data (evaluation of the Edmonton Frail Scale) was performed. Results. In total, 60 HD pts. (70% male and 30% female) participated in our study. The mean age of pts. was 60.1 ± 13.6 years and the median duration of HD was 69 mo (52.5-90). Rate of frailty was 46.7% (n = 28) Older age directly correlated with frailty ( p = 0.01 R = 0.305 ) . The hospitalization rate > 2 times per year directly correlated with the total score of the Edmonton Frail Scale higher results were observed in comparison with non-hospitalized pts. (6 (5-9) vs 5 (3-6); P = 0.037 ) During follow-up period of 45 mo, 18 (30%) patients died, more than a half of them (n = 10; 55.6%) had frailty, but there was no significant correlation between frailty and mortality (p > 0.05) . Conclusions. 1. Rate of frailty prevalence was 46.7% of patients: slight - 31.7%, moderate - 8.3% and severe 6.7%. 2. The significant direct correlation between higher total frailty score and higher hospitalisation rate was found (p = 0.037) but there was no association between frailty and mortality (p = 0.534) .

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)0.001
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 score0.030
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.279
Teacher spread0.249 · 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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