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Record W4406553304 · doi:10.5527/wjn.v14.i1.101480

Distressing symptoms and health-related quality of life in patients with chronic kidney disease

2025· article· en· W4406553304 on OpenAlexaboutno aff
Maysoon S. Abdalrahim, Manal Al-Sutari

Bibliographic record

VenueWorld Journal of Nephrology · 2025
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDistressingQuality of life (healthcare)DiseaseKidney diseaseIntensive care medicinePathologyInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND Chronic kidney disease (CKD) is an incapacitating illness associated with distressing symptoms (DS) that have negative impact on patients’ health-related quality of life (HRQOL). AIM To assess the severity of DS and their relationships with HRQOL among patients with CKD in Jordan. METHODS A descriptive cross-sectional design was used. A convenience sampling approach was used to recruit the participants. Patients with CKD (n = 140) who visited the outpatient clinics in four hospitals in Amman between November 2021 and December 2021 were included. RESULTS The Edmonton Symptom Assessment System was used to measure the severity of the DS while the Short Form-36 tool was used to measure the HRQOL. Participants’ mean age was 50.9 (SD = 15.14). Most of them were males (n = 92, 65.7%), married (n = 95, 67.9%), and unemployed (n = 93, 66.4%). The highest DS were tiredness (mean = 4.68, SD = 2.98) and worse well-being (mean = 3.69, SD = 2.43). The highest HRQOL mean score was for the bodily pain scale with a mean score of 68.50 out of 100 (SD = 32.02) followed by the emotional well-being scale with mean score of 67.60 (SD = 18.57). CONCLUSION Patients with CKD had suboptimal HRQOL, physically and mentally. They suffer from multiple DS that have a strong association with diminished HRQOL such as tiredness and depression. Therefore, healthcare providers should be equipped with the essential knowledge and skills to promote individualized strategies that focusing on symptom management.

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)0.000
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 score0.013
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.285
Teacher spread0.273 · 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

Citations9
Published2025
Admission routes1
Has abstractyes

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