Depression and nonadherence predict mortality in hemodialysis treated end‐stage renal disease patients
Bibliographic record
Abstract
The scientific evaluation of depression's impact on mortality in hemodialysis (HD) patients has yielded mixed results, with the more recent, more rigorous studies detecting a significant relationship. In this study, 130 HD patients from an urban North American hospital were evaluated for depressive affect and then observed for up to 5 years. In a corrected Cox regression model, which held constant age, gender, dialysis vintage, illness severity and diabetic status, depressive affect emerged as a modest but significant predictor of mortality (relative risk = 1.05, 95% confidence interval = 1.01-1.08). When the subjects were divided according to depressive affect severity, those with severe depressive affect had significantly shorter time to death (β = 0.452, P = 0.044). In a subgroup of 85 subjects, self-reported medication adherence was also predictive of mortality, with higher rates of nonadherence being associated with increased mortality risk. This paper lends support to the burgeoning literature on depression and reduced survival in HD populations, as well as begins the investigation of understanding the underlying mechanisms.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".