Depressive symptoms, frailty, and mortality among dialysis patients
Bibliographic record
Abstract
INTRODUCTION: Frailty and depression are highly prevalent in the dialysis population, but the association between them, the risk factors for their development, and their independent associations with mortality have not been studied. METHODS: We examined 771 patients enrolled in the ACTIVE/ADIPOSE prevalent dialysis cohort study. Fried's frailty phenotype and the Center for Epidemiologic Studies Depression score were used to determine frailty and presence of depressive symptoms, respectively. We assessed the baseline association between frailty and depressive symptoms, whether one entity is a risk factor for development of the other, and associations between frailty and depressive symptoms with mortality. FINDINGS: At baseline, 13.1% of our population screened positive for depressive symptoms, 21.8% met criteria for frailty, and 10.0% met criteria for both. During follow-up, 26.6% of our population developed frailty and 12.7% developed depressive symptoms. Using multivariable logistic regression, baseline depressive symptoms were associated with 2.14-fold higher odds of being frail at baseline (95% confidence interval [CI] 1.45-3.17) and with a 2.16-fold higher odds of incident frailty during follow-up (95% CI 1.22-3.82). However, baseline frailty was not associated with incident depressive symptoms. Frailty and depressive symptoms were independent predictors of mortality in time-varying survival analysis (meeting frailty criteria: hazard ratio [HR] 1.53, 95% CI 1.05-2.23; depressive symptoms: HR 2.21, 95% CI 1.50-3.25). DISCUSSION: Frailty and depressive symptoms remained highly prevalent over time and were strongly associated with one another and independently associated with mortality among dialysis patients. Future studies should investigate whether interventions for depression could potentially mitigate the appearance of frailty and its associated poor outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".