Association between changes in quality of life and mortality in hemodialysis patients: results from the DOPPS
Bibliographic record
Abstract
Background: Cross-sectional health-related quality of life (HR-QOL) measures are associated with mortality in hemodialysis (HD) patients. The impact of changes in HR-QOL on outcomes remains unclear. We describe the association of prior changes in HR-QOL with subsequent mortality among HD patients. Methods: A total of 13 784 patients in the Dialysis Outcomes and Practice Patterns Study had more than one measurement of HR-QOL. The impact of changes between two measurements of the physical (PCS) and mental (MCS) component summary scores of the SF-12 on mortality was estimated with Cox regression. Results: Mean age was 62 years (standard deviation: 14 years); 59% were male and 32% diabetic. Median time between HR-QOL measurements was 12 months [interquartile range (IQR): 11, 14]. Median initial PCS and MCS scores were 37.5 (IQR: 29.4, 46.2) and 46.4 (IQR: 37.2, 54.9); median changes in PCS and MCS scores were -0.2 (IQR: -5.5, 4.7) and -0.1 (IQR: -6.8, 5.9), respectively. The adjusted hazard ratio (HR) for a 5-point decline in HR-QOL score was 1.09 [95% confidence interval (CI): 1.06-1.12] for PCS and 1.05 (95% CI: 1.03-1.08) for MCS. Adjusting for the second QOL score, the change was not associated with mortality: HR = 1.01 (95% CI: 0.98-1.05) for delta PCS and 1.01 (95% CI: 0.98-1.03) for delta MCS. Categorizing the first and second scores as predictors, only the second PCS or MCS score was associated with mortality. Conclusions: In our study, only the most recent HR-QOL score was associated with mortality. Hence, the predictive power of a measurement of HR-QOL is not affected by changes in HR-QOL prior to that measurement; more frequent HR-QOL measurements are needed to improve the prediction of outcomes in HD. Further studies are needed to determine the optimal frequency and appropriate instrument to be used for serial measurements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".