Depression of caregivers is significantly associated with depression and hospitalization of hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: The current study aims to elucidate the relationships of depression of caregivers with depression of hemodialysis patients and determine predictors of hospitalization of hemodialysis patients. METHODS: The single-center, cross-sectional study consisted of 200 pairs of eligible hemodialysis patients and caregivers from January 2019 to January 2020. Depression was evaluated using Hospital Anxiety and Depression Scale (HADS) questionnaire. FINDINGS: There were 89 hemodialysis patients with depression (44.5%) and 74 caregivers with depression (37.0%). In multi-variable logistic regression analysis, the hemodialysis patients with depressed caregivers were at increased risk of depression after adjusting for potential confounders (OR = 2.36, p = 0.04). Depression of hemodialysis patients (β = 0.51, p = 0.00) and depression of caregivers (β = 0.36, p = 0.04) were predictors of hospitalization of hemodialysis patients. DISCUSSION: Depression was prevalent among hemodialysis patients and their caregivers. Depression of caregivers was a risk factor for depression and hospitalization of hemodialysis patients. Implementation of appropriate screening programs and specific interventions for depression of hemodialysis patients and their caregivers is required.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| 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".