Bibliographic record
Abstract
PURPOSE OF REVIEW: In renal transplantation, significant improvements in short-term allograft survival have been accomplished, but do not translate into comparable extension of long-term function. Thus, late allograft failure is the challenge while the underlying disease processes are mostly elusive. The purpose of this review is to summarize new diagnostic insights into the identification of specific causes of late allograft failure. RECENT FINDINGS: In 2005, the Banff working group for allograft pathology eliminated the term 'chronic allograft nephropathy'. This became necessary due to the fact that this generic term, summarizing all disease processes causing chronic allograft damage, mutated in the literature into an entity explaining most kidney allograft failures. Since 2005, pathologists have been urged to assign a specific diagnosis instead of using the nonspecific term chronic allograft nephropathy. Simultaneously, considerable research efforts (i.e. the Genome Canada Project and Deterioration of Kidney Allograft Function study) were implemented to identify specific causes of renal allograft failure. In 2009, results from these initiatives were presented indicating that antibody-mediated rejection and recurrent/de-novo glomerulonephritis are the major causes of late renal allograft failure. SUMMARY: With new diagnostic tools available, a disease-specific approach in renal allograft damage becomes feasible. This will allow for designing entity-specific trials and establishment of specific treatments, eventually improving long-term allograft function.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".