Bibliographic record
Abstract
Advances in the technology and delivery of renal replacement therapy (dialysis and transplantation) have revolutionised the outcome of patients with progressive renal disease. However, the paradox of this success has been to uncover a greatly increased risk of cardiovascular disease (CVD), up to 20 times that of the normal population, a pattern similar to that seen in diabetes following the discovery of insulin. However, the magnitude of the problem is greater in renal disease and there is less agreement on the mechanisms or evidence on which to base interventional strategies. The importance of CVD in this population is reflected by recent publications1-3 and a report from a specific task force of the US National Kidney Foundation. The recognition that large scale outcome studies are required has resulted in the initiation of several studies that will report over the next few years. This review is a personal view in which we will cover the background to CVD at different stages in the natural history of progressive renal disease, current treatments, unresolved problems, and ongoing studies To appreciate the problems and management of CVD in progressive renal disease it is necessary to consider the key differences between patients with renal disease and other patient groups. The first is the course of renal disease (fig 1). Patients with progressive renal disease suffer a period of deteriorating renal function, over months to many years (depending on the underlying disease) and leading ultimately to end stage renal disease (ESRD) in a proportion of patients. Most patients with ESRD (around 100 per million population per annum) currently enter renal replacement therapy programmes involving either peritoneal dialysis or haemodialysis. Thereafter, approximately one third will be considered for renal transplantation and, over a period of years, the majority of these will proceed to have a successful cadaveric …
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".