SP643CANONICAL CORRELATION BETWEEN MEDICAL PSYCHOSOCIAL FACTORS AND QUALITY OF LIFE IN HEMODIALYSIS PATIENTS
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Patients with end-stage renal disease have a significant impairment in quality of life (QoL). Most previous studies have focused on medical factors mainly. However, quality of life can also be affected by psychosocial problems in the circumstances of chronic illness. The aim of this study was to identify the associations among psychosocial factors, medical factors and QoL in patients with end-stage renal disease (ESRD). METHODS: The study included 101 patients with ESRD who were undergoing HD (55 males with mean age 57.1 ± 12.1 years). Psychosocial factors were evaluated using the Hospital Anxiety and Depression Scale (HADS), Multidimensional Scale of Perceived Social Support (MSPSS), Montreal Cognitive Assessment (MoCA) and Pittsburgh Sleep Quality Index (PSQI). In addition, for evaluating caregivers’ burden in part of psychosocial factors, HADS and Zarit Burden Interview (ZBI) of main caregivers were administered. We also accessed medical factors (Kt/V and urea reduction ratio as markers of dialysis adequacy, normalized protein catabolic rate and duration of HD) with laboratory results (body mass index, albumin, hemoglobin, calcium, phosphorus, vitamin D, ferritin). The quality of life was evaluated using WHO Quality of Life-BREF (WHOQOL-BREF). Canonical correlation analysis was used to investigate the patterns of associations. RESULTS: The canonical correlation between psychosocial factor and QoL was significant. The first canonical correlation was 0.673 (proportion = 60.6%, p = 0.001) and the second was 0.519 (proportion = 26.9%, p = 0.006). However, the canonical correlation between medical factor and QoL was not significant (the first: p = 0.586, the second: p = 0.713). CONCLUSIONS: The QoL of patients with ESRD was not associated with medical factor, but psychosocial factor in canonical correlation analysis. This finding may suggest that medical workers should recognize and treat psychosocial problems as well as clinical problems. We also would like to emphasize the comprehensive approach with cooperation between psychiatrists and nephrologists for improvement of QoL in ESRD 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 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".