Challenges and future of renal replacement therapy
Bibliographic record
Abstract
Renal community increasingly recognizes the challenges of very high mortality, morbidity and low quality of life among dialysis patients. Current hemodialysis (HD) schedule provides less than 10% of the clearance power of the natural kidneys and therefore current standard HD treatment is still a long way from providing adequate renal replacement. In the future it would be expected to improve dialysis control with the development of new technology: membranes, dialysate buffer, electrolyte concentration, system interface, arteriovenous access monitoring. Online technology must be adapted to routine HD for new prescription monitoring such as creatinine online sensing technology, sodium-specific electrode or hydrogen ion concentration. Online convective therapies offer the opportunity for a more biological renal replacement therapy increasing convective transport in order to "reproduce" glomerular function and improving small and middle molecular clearance in an economically feasible and safe way. The paradigm of thrice-weekly dialysis is faced with diminishing returns, with the possible exception of long dialysis sessions. More frequent (daily) dialysis represents a very promising tool for improving dialysis outcomes and quality of life. Future technologies for renal replacement include bioartificial kidneys based in continuous hemofiltration and bioartificial tubules. Although Phase I/II clinical trial on 10 patients with acute renal failure has been reported the procedure requires further evaluation. Organogenesis, therapeutic cloning, or cloning and organogenesis combined might in the future produce a functional and histocompatible kidney. The continuous increase in incidence and prevalence of renal-replacement therapy is a world-wide phenomenon, although the rates in Europe are still much lower than in the U.S. The increase in rates applies especially to older patients, patients with diabetes mellitus and renal vascular disease and the consequences of this important comorbidity are very important in terms of mortality.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".