Bibliographic record
Abstract
End-stage renal disease (ESRD) continues to be a disease process with a high rate of hospitalization and mortality. There has been little innovation in nephrology over the last few decades compared to revolutionary high-tech advancements in other areas like oncology and cardiovascular medicine. Kidney transplantation, the only available alternative to renal replacement therapy, is limited in its availability. It is essential to have advances in this field to improve the efficiency of currently available treatments and devise new therapies. The current description of renal replacement therapy is inappropriate as it only replaces the filtration function of the failed kidney without addressing its other vital metabolic, endocrinologic, and immunologic roles and portability. Hence, it is critical to have newer therapies focusing on total replacement and portability, not just clearance. This review will address the developments in hemodialysis therapy. Advances in hemodialysis therapy include hemodiafiltration, portable machines, wearable artificial kidneys, and bioartificial kidneys. Although promising, newer technologies in this direction are still far from clinical application. Several organizations and enterprises including the Kidney Health Initiative and Kidney X: The Kidney Innovation Accelerator, as well as The Advancing American Kidney Health Initiative, are working in tandem to develop new therapies that could customize the treatment of ESRD.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".