Depression and Advance Care Planning Among Japanese Patients Undergoing Hemodialysis: Japanese Dialysis Outcomes and Practice Pattern Study (J-DOPPS)
Bibliographic record
Abstract
Rationale & Objective: Advance care planning (ACP) is crucial in end-of-life care. Data on ACP discussion among patients with end-stage kidney disease are limited. One study has suggested that depressive symptoms increase ACP discussion. Study Design: This study aimed to analyze the association between depression and ACP discussion in patients undergoing hemodialysis. Setting & Population: This used data from the Japan Dialysis Outcomes and Practice Patterns Study. Predictor: Both cross-sectional and longitudinal associations between depressive symptoms and ACP discussion were examined. Outcomes: Depressive symptoms were defined as a score of ≥10 points on the 10-item Center for Epidemiologic Studies Depression scale. ACP discussion was defined as discussing ACP with health care providers and family members. Analytical Approach: Generalized estimating equations and generalized linear models based on Poisson distribution and log-link function were used to estimate prevalence (PR) and incidence proportion ratios (IPRs) using robust standard errors, respectively. Results: Data in 2016 and 2017 included 2,443 patients for the cross-sectional analysis and 870 for the longitudinal analysis. ACP discussion was 26% in 2016 and 28% in 2017, with depressive symptoms rates of 45% and 47%, respectively. The cross-sectional analysis indicated a positive association between depressive symptoms and ACP discussion (adjusted PR, 1.20; 95% confidence interval (CI), 1.05-1.37). Depressive symptoms were not significantly associated with ACP discussion in longitudinal analyses (adjusted IPR, 1.10; 95% CI 0.80-1.51). Limitations: Sample size, unadjusted confounding, and generalizability across cultural backgrounds. Conclusions: Our study showed an association between depressive symptoms and ACP in the cross-sectional analysis, but not longitudinally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".