Chronic kidney disease progression in native and transplant kidneys
Bibliographic record
Abstract
PURPOSE OF REVIEW: The present review addresses the recent literature demonstrating important differences in the rate of progression of kidney function decline between transplant recipients and patients with native kidney disease. It also highlights the need for prospective studies to determine the importance of nonimmune factors that are established risk factors for progression of native kidney disease in the transplant setting. RECENT FINDINGS: Transplant recipients establish modest levels of kidney function but have rates of kidney function decline that are slower than those in patients with native kidney disease. Continued improvements in long-term graft survival have not been achieved despite significant advances in immunosuppression. There is increasing observational evidence that nonimmune factors that play a causal role in progression of native kidney disease may also be important determinants of allograft decline. There are fundamental differences between transplant recipients and patients with native kidney disease that preclude extrapolation of evidence from native kidney disease to the transplant setting. SUMMARY: Transplant recipients are a unique group of chronic kidney disease patients. Prospective studies to determine the importance of nonimmune factors such as hypertension, proteinuria, dyslipidemia, diabetes, and anemia in the transplant setting are needed.
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) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".