The relationship between frailty levels and quality of life in patients over 65 years of age receiving regular hemodialysis treatment
Bibliographic record
Abstract
BACKGROUND: This study was conducted to investigate the relationship between frailty levels and quality of life in patients over 65 years of age receiving regular haemodialysis treatment. MATERIALS AND METHODS: The study was designed as a descriptive and correlational study. Data were collected from patients in a university hospital and two private dialysis centres in Konya between August and September 2023. The study sample consisted of 171 patients. The data collection tools included the Descriptive Characteristics Information Form, the Edmonton Frail Scale (EFS), and the EQ-5D-5L Quality of Life Questionnaire. Data analysis was performed using the SPSS software program. Frequency and percentage calculations were obtained for the measurements in the personal information form. Since the data in the personal information form and EFS did not show normal distribution, non-parametric tests, specifically the Mann-Whitney U-test and the Kruskal-Wallis H-test, were used. RESULTS: The results obtained in the present study showed a significant, moderate, negative correlation between the quality of life and frailty levels of patients over 65 years of age receiving dialysis treatment. CONCLUSION: This study demonstrated that as quality of life increased, frailty levels decreased. Improvements in patients' quality of life could potentially lead to a reduction in frailty levels.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".