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Quality of life, self‐care ability, and sense of coherence in hemodialysis patients: A comparative study

2005· article· en· W2127953933 on OpenAlexvenueno aff
Maria Ageborg, Britt‐Louise Allenius, Claes Cederfjäll

Bibliographic record

VenueHemodialysis International · 2005
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHemodialysisDialysisQuality of life (healthcare)Sense of agencyEnd stage renal diseaseSelf careHome hemodialysisRenal replacement therapyModalitiesHealth careIntensive care medicinePhysical therapyFamily medicineNursingInternal medicinePsychology

Abstract

fetched live from OpenAlex

The number of patients treated for end-stage renal disease has increased in Sweden as in the rest of the world. During the last 6 years, more than 1000 people per year started renal replacement therapy in Sweden. Today hemodialysis (HD) patients have the opportunity to choose from different treatment modalities, home HD, self-care dialysis or conventional dialysis. The aim of the study was to investigate whether there are differences in the way HD patients view their quality of life, self-care ability and sense of coherence if they dialyze themselves at home, dialyze themselves in center (self-care), or if they are dialyzed by nurses in an outpatient dialysis unit. The instruments consisted of the Short Form 36 (SF-36) health survey, the Appraisal of Self-Care Agency questionnaire and the Sense of Coherence questionnaire. Nineteen patients participated in the study (5 patients on home HD, 6 self-care patients and 8 patients on conventional in center dialysis). There was a tendency for those who dialyzed at home to score higher on quality of life, self-care ability and sense of coherence than those who dialyzed themselves in center. Since the number of participants in this study was low, it is necessary for future studies to include more patients to verify the results.

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 score0.011
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.333
Teacher spread0.303 · 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

Citations40
Published2005
Admission routes1
Has abstractyes

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