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Record W3203166911 · doi:10.37551/s2254-28842021026

Síntomas asociados al sufrimiento en pacientes con enfermedad renal crónica en hemodiálisis

2021· article· es· W3203166911 on OpenAlexaboutno aff
Claudia Ramírez-Rodríguez, Yadira Grau Valdés, Jorge A. Grau-Abalo

Bibliographic record

VenueEnfermería Nefrológica · 2021
Typearticle
Languagees
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesMedicinePhilosophy

Abstract

fetched live from OpenAlex

Introduction: Insufficiently controlled symptoms could be a determining factor or aggravation of suffering in patients undergoing hemodialysis. However, research about this topic is not enough. Objectives: This study aims to describe suffering according to severity of symptoms in patients with End-Stage Renal Disease undergoing hemodialysis. Materials and Method: The study is transversal, descriptive and observational with some tasks of correlation in a sample of 31 patients. The assessment tools were; the interview, the instrument to detect wellbeing/disturbance proposed by Bayés and collaborators and a series of subscales of the Edmonton Symptom Assessment Scale/ESAS to identify the intensity and frequency of symptoms that could be associated to suffering. Results: 87.1 % of the sample showed a low presence of symptoms with severe intensity associated to suffering in the last 24 hours, 77.4% showed low presence in the last week and 61.3 % showed a low presence in a period of one month. No significant association was found between the presence of symptoms with severe intensity associated to suffering in 24 hours. Conclusions: The majority of subjects were characterized by low presence of symptoms with severe intensity associated to that suffering. The magnitude of a set of symptoms did not result in an important factor associated to the suffering in these patients.

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)0.001
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 score0.354
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.298
Teacher spread0.283 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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