Co-morbidity and quality of life in chronic kidney disease patients
Bibliographic record
Abstract
Chronic kidney disease (CKD) is frequently associated with other chronic medical conditions. Adjusting for potential confounding factors that are associated with the outcome of interest is important both in clinical research and in everyday clinical practice. Comorbidity is such an important co-variable that it is reported to predict different outcomes in patients with ESRD. Health related quality of life (HRQoL) has increasingly been recognized as an important aspect of health care delivery, measure of effectiveness and patient experience, in chronic medical conditions. The progressively older ESRD patient population of industrialized countries is significantly debilitated by the burden of disease and also by the intrusiveness of renal replacement therapies. For these patients simply prolonging life is not enough. Little information has been published about the association of comorbidity and HRQoL. The aim of this review is to summarize the significance of comorbidity in patients with ESRD, with a special focus on the complex relationship between comorbidity and HRQoL. Several frequently used instruments will be described and the current literature, that compared the relative utility and accuracy of these tools, will be reviewed. Finally, the impact of selected medical conditions on HRQoL of patients with end-stage renal disease will be demonstrated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".