Epidemiology of haemodialysis outcomes
Bibliographic record
Abstract
Haemodialysis (HD) is the commonest form of kidney replacement therapy in the world, accounting for approximately 69% of all kidney replacement therapy and 89% of all dialysis. Over the last six decades since the inception of HD, dialysis technology and patient access to the therapy have advanced considerably, particularly in high-income countries. However, HD availability, accessibility, cost and outcomes vary widely across the world and, overall, the rates of impaired quality of life, morbidity and mortality are high. Cardiovascular disease affects more than two-thirds of people receiving HD, is the major cause of morbidity and accounts for almost 50% of mortality. In addition, patients on HD have high symptom loads and are often under considerable financial strain. Despite the many advances in HD technology and delivery systems that have been achieved since the treatment was first developed, poor outcomes among patients receiving HD remain a major public health concern. Understanding the epidemiology of HD outcomes, why they might vary across different populations and how they might be improved is therefore crucial, although this goal is hampered by the considerable heterogeneity in the monitoring and reporting of these outcomes across settings. This Review examines the epidemiology of haemodialysis outcomes — clinical, patient-reported and surrogate outcomes — across world regions and populations, including vulnerable individuals. The authors also discuss the current status of monitoring and reporting of haemodialysis outcomes and potential strategies for improvement.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".