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Record W4413730632 · doi:10.1111/ijn.70042

Multidimensional Determinants of Frailty in Haemodialysis Patients: The Overlooked Roles of Depression and Cognitive Function

2025· article· en· W4413730632 on OpenAlexaboutno aff
Seçil Beyece İncazlı, Gülseren Keskin, Sema Üstündağ, Neslihan Tezcan, Ebru Sezer

Bibliographic record

VenueInternational Journal of Nursing Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)Marital statusMedicineCognitionBeck Depression InventoryQuality of life (healthcare)PersonalityClinical psychologyGeriatric Depression ScaleGerontologyDepressive symptomsPsychiatryPsychologyAnxiety

Abstract

fetched live from OpenAlex

AIM: This study aims to examine the level of frailty in patients undergoing haemodialysis treatment and investigate the effects of sociodemographic, psychological and clinical variables on frailty. METHOD: A cross-sectional and correlational research design was employed with 386 haemodialysis patients over the age of 50. Data were collected using the Edmonton Frail Scale, Beck Depression Inventory, Standardized Mini-Mental Test and Eysenck Personality Questionnaire. RESULTS: The study revealed that 48.4% of haemodialysis patients fell into the 'apparently frail' category, and frailty levels were significantly associated with age, depression and cognitive functions (p < 0.05). However, no significant effect of gender, marital status, educational level, chronic diseases or personality traits on frailty was identified. CONCLUSIONS: Most haemodialysis patients were found to be apparently frail, with frailty levels increasing with age. Furthermore, frailty was linked to higher levels of depressive symptoms and lower cognitive function. Evaluating depression and cognitive function is crucial for alleviating frailty symptoms and improving 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 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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.359
Teacher spread0.339 · 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
Published2025
Admission routes1
Has abstractyes

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