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Quality of life in chronic hemodialysis patients in Russia

2006· article· en· W2032331817 on OpenAlexvenueno aff
I. А. Vasilieva

Bibliographic record

VenueHemodialysis International · 2006
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHemodialysisNeuroticismDepression (economics)AnxietyPopulationQuality of life (healthcare)DialysisMental healthInternal medicineCross-sectional studyPhysical therapyPsychiatryPersonalityPsychology

Abstract

fetched live from OpenAlex

The aim of this cross-sectional study was to compare health-related quality of life (HRQOL) of Russian hemodialysis (HD) patients with the general population and international data, and to determine factors influencing HRQOL. One thousand forty-seven HD patients from 6 dialysis centers were studied (576 male, age 43.5 +/- 12.5 years, HD duration 55.0 +/- 47.2 months). Health-related quality of life was evaluated by SF-36. Self-appraisal Depression Scale (W. Zung), State-Trait Anxiety Inventory, and Level of Neurotic Asthenia Scale were used. Hemodialysis patients scored significantly lower than the general Russian population in the majority of SF-36 scales. The only exception was the Mental Health score, which was even better than the general population. The Mean physical component score (PCS) of HD patients was 36.9 +/- 9.7, and the mental component score was (MCS) 44.2 +/- 10.5. In multiple linear regression analysis, increasing age, HD duration, depression level and number of days of hospitalization in the past 6 months were significant independent predictors of low PCS along with a low level of serum albumin. Advancing age was also a predictive factor for low MCS along with increase of HD duration, depression level, trait anxiety, and level of asthenia. As far as we know, this is the first study to report on HRQOL of a large sample of Russian HD patients performed using SF-36. Compared with the general population, Russian HD patients had significantly lower scores on the majority of SF-36 scales, especially in the physical domain. The mean PCS and MCS were comparable with European data for HD patients. A number of demographic, clinical, and psychological variables affect HRQOL.

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 score0.026
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.288
Teacher spread0.272 · 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

Citations57
Published2006
Admission routes1
Has abstractyes

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