Bibliographic record
Abstract
PURPOSE OF REVIEW: Recurrent glomerulonephritis is the third most common cause of graft failure, ranking only behind immunologic rejection and death with a functioning graft. Knowledge of the rates and timing of recurrent glomerular disease are important in counseling potential transplant recipients and preventive and therapeutic treatment strategies are necessary for those patients at risk. RECENT FINDINGS: Large observational studies that have analyzed posttransplant biopsies have confirmed the high rates of glomerular disease recurrence in renal allografts. Newer immunosuppressive protocols over the past 10 years have not affected the rate of disease recurrence or graft loss. There is emerging evidence that rituximab may be efficacious in treating recurrent membranous nephropathy and focal segmental glomerulosclerosis; however, larger clinical trials are warranted. SUMMARY: Recurrent glomerulonephritis is an important determinant of long-term outcomes after transplantation, requiring appropriate counseling to potential transplant recipients. Currently, there are no proven strategies to prevent recurrent glomerulonephritis in renal transplant recipients. Despite the high rates of recurrent disease, long-term graft survival is still very good and transplantation remains the best treatment option for patients with end-stage renal disease from primary glomerulonephritis.
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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".