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Record W2072377576 · doi:10.1016/j.nephro.2013.07.200

Évaluation prospective de la douleur à la ponction de la fistule artérioveineuse en hémodialyse chronique

2013· article· fr· W2072377576 on OpenAlexaboutno aff
S. Dahri, B. Doukkali, S. Jaafour, A. El Hassani, M. Arrayhani

Bibliographic record

VenueNéphrologie & Thérapeutique · 2013
Typearticle
Languagefr
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Depression (economics)AnxietyPhysical therapyHemodialysisCardiorespiratory fitnessMental healthDialysisInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Dialysis patients experience tremendous symptom burden and substantial impaired health-related quality of life (HRQL).We determined the association between symptom burden and HRQL in 591 hemodialysis patients.Patients completed the modified Edmonton Symptom Assessment System and the Kidney Dialysis Quality of Life Short Form at baseline and after six months.There were no demographic, serological, or dialysis-related predictors for either HRQL or symptom burden. Pain, tiredness, lack of well-being, and depression were the only independent predictors of mental HRQL, accounting for 42.5% of the variation in the baseline mental health composite (MHC). Pain, fatigue, lack of well-being, and shortness of breath were the only independent predictors of physical HRQL, accounting for 38.5% of the variation in the baseline physical health composite (PHC). After follow-up, only changes in depression, anxiety, tiredness, and lack of appetite were independently associated with a change in MHC score, accounting for 48.7% of the variability. Only changes in pain, tiredness, and lack of appetite were independently associated with a change in PHC, accounting for 44.6% of the variability in the final multivariate regression model. No change in biochemical parameters predicted a change in either the MHC or the PHC.Symptom burden in end-stage renal disease was substantial and had a tremendous negative impact on all aspects of hemodialysis patients' HRQL. These patients, therefore, would likely benefit from the institution of programs to reduce symptom burden.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.304
Teacher spread0.288 · 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.

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
Published2013
Admission routes1
Has abstractyes

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