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Prevalencia y evaluación de síntomas en enfermedad renal crónica avanzada

2015· article· es· W2212328233 on OpenAlexaboutno aff
Daniel Gutiérrez Sánchez, Juan P. Leiva-Santos, Rosa M. Sánchez-Hernández, Rafael Gómez García

Bibliographic record

VenueEnfermería Nefrológica · 2015
Typearticle
Languagees
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecologyHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

The patient with advanced chronic kidney disease (ACKD) has a high symptom burden that contribute to increased suffering and diminish their quality of life. The use of symptom assessment tools is essential for the control of symptoms. The aim of this review is to obtain a contrasted vision of the instruments commonly used to assess symptoms in ACKD, making a description of the prevalence of symptoms in this population. Method: A review of the literature on studies in which an instrument is used to measure the intensity of several symptoms in patients with ACKD was undertaken. The search was conducted in PubMed, Cochrane, SciELO and TESEO. Inclusion criteria were: studies in patients with ACKD, evaluating symptoms with an assessment tool, and also indicate the prevalence of various symptoms. Results: The instruments identified were the Memorial Symptom Assessment Scale Short Form (MSAS-SF), the Dialysis Symptom Index (DSI), the Edmonton Symptom Assessment System (ESAS) and the Palliative Care Outcome Scale-Symptoms Kidney (POS-S RENAL). In adult patients with ACKD undergoing renal replacement therapy with hemodialysis and peritoneal dialysis, the most prevalent symptoms were fatigue, pruritus, constipation, anorexia, pain, sleep disturbance, anxiety, dyspnea, nausea, restless legs, and depression. These symptoms were similar in patients with renal conservative management, and showed a common pattern to the symptoms of others advanced diseases. We conclude that we need to research about the prevalence and evaluation of symptoms in this population, and a systematic use of specific instruments for evaluating symptoms as an outcome measure is necessary.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.005

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 head0.035
GPT teacher head0.338
Teacher spread0.304 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations16
Published2015
Admission routes1
Has abstractyes

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