Síntomas asociados al sufrimiento en pacientes con enfermedad renal crónica en hemodiálisis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".