Longitudinal associations of depressive symptoms and pain with quality of life in patients receiving chronic hemodialysis
Bibliographic record
Abstract
Depressive symptoms and pain are common in patients on chronic hemodialysis (HD), yet their associations with quality of life (QOL) are not fully understood. We sought to characterize the longitudinal associations of these symptoms with QOL. As part of a trial comparing two symptom management strategies in patients receiving chronic HD, we assessed depressive symptoms using the Patient Health Questionnaire-9 (PHQ-9), and pain using the Short Form McGill Pain Questionnaire (SF-MPQ) monthly over 24 months. We assessed health-related QOL (HR-QOL) quarterly using the Short Form 12 (SF-12) and global QOL (G-QOL) using a single-item survey. We used random effects linear regression to analyze the independent associations of depressive symptoms and pain, scaled based on 5-point increments in symptom scores, with HR-QOL and G-QOL. Overall, 286 patients completed 1417 PHQ-9 and SF-MPQ symptom assessments, 1361 SF-12 assessments, and 1416 G-QOL assessments. Depressive symptoms were independently and inversely associated with SF-12 physical HR-QOL scores (β = -1.09; 95% confidence interval [CI]: -1.69, -0.50, P < 0.001); SF-12 mental HR-QOL scores (β = -4.52; 95% CI: -5.15, -3.89, P < 0.001); and G-QOL scores (β = -0.64; 95%CI: -0.79, -0.49, P < 0.001). Pain was independently and inversely associated with SF-12 physical HR-QOL scores (β = -0.99; 95% CI: -1.30, -0.68, P < 0.001) and G-QOL scores (β = -0.12; 95%CI: -0.20, -0.05, P = 0.002); but not with SF-12 mental HR-QOL scores (β = -0.16; 95%CI: -0.050, 0.17, P = 0.34). In patients receiving chronic HD, depressive symptoms and to a lesser extent pain, are independently associated with reduced HR-QOL and G-QOL. Interventions to alleviate these symptoms could potentially improve patients' HR-QOL and G-QOL.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".