Impact of Thirst Perception on Health‐Related Quality of Life in Hemodialysis Patients: A Multicenter Study
Bibliographic record
Abstract
INTRODUCTION: Thirst distress is a common yet underexplored symptom among hemodialysis (HD) patients, with limited understanding of its impact on quality of life. This study aims to evaluate thirst perception, identify factors associated with its intensity, and examine its relationship with quality of life in a multicenter cohort of HD patients. METHODS: This cross-sectional analysis utilized baseline data from the Hemodiafiltration on Physical Activity and Self-Reported Outcomes: A Randomized Controlled Trial (HDFit). Participants were over 18 years old from 13 dialysis units across Brazil. Thirst perception was assessed using the Dialysis Thirst Inventory (DTI) questionnaire, and health-related quality of life (HRQoL) was measured with the SF-36 questionnaire. We compared participants with low versus high thirst perception based on the median DTI score and conducted multiple regression analysis to identify independent determinants of physical and mental HRQoL components. FINDINGS: The study sample comprised 195 patients (male: 71%; median age: 54 [41-66] years; 29% with diabetes) from 13 dialysis centers, with chronic HD duration up to 24 months. The median DTI score was 17 (14-22). Participants with higher thirst perception (DTI > 17) were younger, had a higher prevalence of lower income and educational levels, and a lower prevalence of fluid overload. Multiple regression analysis, adjusted for demographic, clinical, and nutritional variables, revealed that increased thirst perception was independently associated with poorer physical and mental HRQoL. CONCLUSION: In a multicenter HD population, higher thirst perception was an independent determinant of diminished health-related quality of life.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".