Association Between Sleep Disturbance and Physical Function Among Patients with Advanced CKD
Bibliographic record
Abstract
Background: Sleep disturbance (SLD) and impaired physical function (PF) are common symptoms in patients with advanced chronic kidney disease (CKD) and are associated with lower health-related quality of life. The relationship between these symptoms remains poorly understood. Methods: Cross-sectional analysis of a convenience sample of adult patients with advanced CKD on kidney replacement therapy (KRT). SLD and PF were assessed using Patient-Reported Outcome Measurement Information System (PROMIS) item banks. Spearman’s rank correlation analysis evaluated the association between SLD (PROMIS T-score ≥ 60) and impaired PF (PROMIS T-score < 40). Univariable and multivariable regression models further assessed this relationship, adjusting for sociodemographic (age, sex, racialized status, immigrant status, material deprivation) and clinical (hemoglobin, albumin, comorbidity, KRT modality) covariates, as well as symptoms (depression, pain interference, fatigue). Multiple imputation by chained equations accounted for missing data. Results: Among 683 participants, mean(SD) age was 59(21) years; 60% were male, 46% white, 58% kidney transplant recipients (KTRs). The mean(SD) SLD T-score was 50(11); 17% reported SLD. The mean(SD) PF T-score was 43(11); 44% reported impaired PF. SLD and PF were negatively correlated, ρ = -0.29, p < .001. This association remained significant after adjusting for sociodemographic and clinical covariates, and even when adjusting for any of depression, pain interference, or fatigue, but not when all three symptoms were included. Participants with SLD had over three times the odds of reporting impaired PF (odds ratio [OR] = 3.05, 95% CI [2.01, 4.66], p < .001). This association remained significant after adjusting for sociodemographic and clinical factors, and after further adjustment for depression or pain interference, but not for fatigue or when all three were included. Conclusion: Higher SLD is associated with lower PF in patients with advanced CKD on KRT. This relationship may be partly mediated by symptoms, particularly fatigue. Further research is needed to determine whether targeted interventions for SLD can significantly impact PF in this population. Funding: Private Foundation Support, Government Support – Non-U.S.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".