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Record W4406410182 · doi:10.1111/psyg.13242

The relationship between frailty levels and quality of life in patients over 65 years of age receiving regular hemodialysis treatment

2025· article· en· W4406410182 on OpenAlexaboutno aff
Sümeyye Dilek, Işın Cantekin

Bibliographic record

VenuePsychogeriatrics · 2025
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsHemodialysisMedicineQuality of life (healthcare)GerontologyIntensive care medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: This study was conducted to investigate the relationship between frailty levels and quality of life in patients over 65 years of age receiving regular haemodialysis treatment. MATERIALS AND METHODS: The study was designed as a descriptive and correlational study. Data were collected from patients in a university hospital and two private dialysis centres in Konya between August and September 2023. The study sample consisted of 171 patients. The data collection tools included the Descriptive Characteristics Information Form, the Edmonton Frail Scale (EFS), and the EQ-5D-5L Quality of Life Questionnaire. Data analysis was performed using the SPSS software program. Frequency and percentage calculations were obtained for the measurements in the personal information form. Since the data in the personal information form and EFS did not show normal distribution, non-parametric tests, specifically the Mann-Whitney U-test and the Kruskal-Wallis H-test, were used. RESULTS: The results obtained in the present study showed a significant, moderate, negative correlation between the quality of life and frailty levels of patients over 65 years of age receiving dialysis treatment. CONCLUSION: This study demonstrated that as quality of life increased, frailty levels decreased. Improvements in patients' quality of life could potentially lead to a reduction in frailty levels.

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)0.001
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 score0.020
Threshold uncertainty score0.268

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.071
GPT teacher head0.345
Teacher spread0.274 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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