Dialysis, cardiovascular disease, and the future
Bibliographic record
Abstract
Abstract Atherosclerosis, particularly coronary atherosclerosis, is accelerated in renal failure, as originally postulated by Belding Scribner. But in contrast to previous opinion, myocardial infarction from coronary heart disease is not the single major cause of cardiac death in dialyzed patients, the most common causes being sudden death and cardiac failure. Apart from coronary heart disease, the following cardiomyopathic features are prevalent and explain a large part of the excess cardiac risk: cardiomyocyte dropout, left ventricular hypertrophy, cardiac interstitial fibrosis, microangiopathy with arteriolar thickening, and capillary deficit as well as reduced ischemia tolerance. Recently, cardiovascular risk factors related to abnormal mineral metabolism, particularly phosphate and vitamin D, have gained unanticipated importance. Controlled evidence has become available concerning intervention with ACE inhibitors, angiotensin receptor blockers, β‐blockers, and statins in dialyzed patients. It is imperative that apart from the “classical” cardiovascular risk factors that do not exhaustively explain the excessive cardiovascular risk in dialyzed patients, novel pathomechanisms are considered and investigated; potential examples include depression, sleep abnormalities, etc. The above arguments do not negate the fact that today's modalities of renal replacement therapy are poor substitutes for the normal kidney's function so that as a result alternative strategies, e.g., daily dialysis, may also dramatically improve cardiovascular risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".