Association between cognitive functioning and health-related quality of life and its mediation by depressive symptoms in older patients with kidney failure
Bibliographic record
Abstract
BACKGROUND: Impaired cognition, poor health-related quality of life (HRQoL) and depressive symptoms are common in older patients with kidney failure. Understanding what influences HRQoL is important, as older patients regard HRQoL as a health priority. This study examines whether cognitive functioning is associated with HRQoL and whether depressive symptoms mediate this effect in older patients with kidney failure. METHODS: were included from the ongoing DIALOGICA study. Cognitive functioning was assessed using the Montreal Cognitive Assessment. Depressive symptoms were screened with 2 Whooley Questions and thereafter assessed with the 15-item Geriatric Depression Scale. HRQoL was assessed using the 12-item Short-Form Health Survey. To assess whether cognitive functioning is associated with HRQoL, cross-sectional multivariable linear regression analyses were performed. Subsequent mediation analyses were performed with PROCESS using the product method. RESULTS: (SD 3.0). Cognitive functioning was associated with mental HRQoL (adjusted β 0.30, 95% CI 0.05;0.55) but not physical HRQoL (adjusted β 0.18, 95% CI -0.09;0.44). This effect is mediated by depressive symptoms (adjusted β 0.14, 95% CI 0.04;0.25). CONCLUSION: Lower cognitive functioning was negatively associated with mental HRQoL, which was mediated by depressive symptoms in older patients with kidney failure. Future research should explore whether cognitive interventions and treatment of depression improve HRQoL in this vulnerable patient population.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".