Sleep disorders, depressive symptoms and health-related quality of life--a cross-sectional comparison between kidney transplant recipients and waitlisted patients on maintenance dialysis
Bibliographic record
Abstract
BACKGROUND: Kidney transplantation is believed to improve health-related quality of life (HRQoL) of patients requiring renal replacement therapy (RRT). Recent studies suggested that the observed difference in HRQoL between kidney transplant recipients (Tx) vs patients treated with dialysis may reflect differences in patient characteristics. We tested if Tx patients have better HRQoL compared to waitlisted (WL) patients treated with dialysis after extensive adjustment for covariables. METHODS: Eight hundred and eighty-eight prevalent Tx patients followed at a single outpatient transplant clinic and 187 WL patients treated with maintenance dialysis in nine dialysis centres were enrolled in this observational cross-sectional study. Data about socio-demographic and clinical parameters, self-reported depressive symptoms and the most frequent sleep disorders assessed by self-reported questionnaires were collected at enrollment. HRQoL was assessed by the Kidney Disease Quality of Life Questionnaire. RESULTS: Patient characteristics were similar in the Tx vs WL groups: the proportion of males (58 vs 60%), mean ± SD age (49 ± 13 vs 49 ± 12) and proportion of diabetics (17 vs 18%), respectively, were all similar. Tx patients had significantly better HRQoL scores compared to the WL group both in generic (Physical function, General health perceptions, Energy/fatigue, Emotional well-being) and in kidney disease-specific domains (Symptoms/problems, Effect- and Burden of kidney disease and Sleep). In multivariate regression models adjusting for clinical and socio-demographic characteristics, sleep disorders and depressive symptoms, the modality of RRT (WL vs Tx) remained independently associated with three (General health perceptions, Effect- and Burden of kidney disease) out of the eight HRQoL dimensions analysed. CONCLUSIONS: Kidney Tx recipients have significantly better HRQoL compared to WL dialysis patients in some, but not all, dimensions of quality of life after accounting for differences in patient characteristics. Utilizing multidimensional disease-specific questionnaires will allow better understanding of treatment, disease and patient-related factors potentially affecting quality of life in patients with chronic medical conditions.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".