Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.002 |
| 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 it