Association between depression symptoms and cognitive frailty in chronic kidney disease patients: a cross-sectional study
Bibliographic record
Abstract
Objective Cognitive frailty (CF) and depressive symptoms are prevalent in patients with chronic kidney disease (CKD) and may synergistically exacerbate adverse health outcomes. This study examined their association to inform early intervention strategies. Methods Demographic and clinical data were collected from CKD patients across three hospitals in Shanghai. Depressive symptoms and CF status were assessed via standardized questionnaires. A logistic regression model and restricted cubic spline (RCS) analysis were employed to evaluate the association between depressive symptoms and CF. Results CF was diagnosed using the Frailty Phenotype (FP), Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating (CDR). Among 800 participants, 317 exhibited CF (prevalence: 39.6%). The adjusted logistic regression model revealed a significant positive association between Center for Epidemiologic Studies Depression (CES-D) scores and CF (OR=1.124, 95% CI : 1.094–1.15 6, p <0.001). RCS analysis demonstrated a nonlinear dose-response relationship: CF prevalence increased with rising CES-D scores until plateauing at a score of 9. The odds ratio (OR) exceeded the statistical significance threshold when CES-D scores reached 12. Subgroup analyses consistently supported this dose-response pattern. Conclusion Depressive symptoms are significantly associated with CF in CKD patients. Routine CES-D screening and provision of psychological support for patients scoring ≥12 may mitigate CF risk.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".