Bibliographic record
Abstract
Acute renal failure (ARF) in the transplanted kidney represents ahigh-stakes area of nephrology and of transplantation practice.A correct diagnosis can lead to rapid return of renal function; an incorrect diagnosis can lead to loss of the graft and severe sequelae for the patient. The diagnostic possibilities are many (Fig. 10-1) and treatments quite different, although the clinical presentations of new-onset functional renal impairment and of persistent nonfunctioning after transplant may be identical. In transplant-related ARF percutaneous kidney allograft biopsy is crucial in differentiating such diverse entities as acute rejection (Figs. 10-2 to 10-9), acute tubular necrosis (Figs. 10-10 to 10-14), cyclosporine toxicity (Figs. 10-15 and 10-16), posttransplant lympho-proliferative disorder (Fig. 10-17), and other, rarer, conditions. In the case of acute rejection, standardization of transplant biopsy interpretation and reporting is necessary to guide therapy and to estab-lish an objective endpoint for clinical trials of new immunosuppressive agents. The Banff Classification of Renal Allograft Pathology [1] is an internationally accepted standard for the assessment of renal allograft biopsies sponsored by the International Society of Nephrology Commission of Acute Renal Failure. The classification had its origins in a meeting held in Banff, Alberta, in the Canadian Rockies, in August, 1991, where subsequent meetings have been held every 2 years. Hot
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".