Bibliographic record
Abstract
The standardization of renal allograft pathology began in 1991 at the first Banff Conference held in Banff, Alberta, Canada. The first task of transplant pathologists, clinicians, and surgeons was to establish diagnostic criteria for T-cell-mediated rejection (TCMR). The histological threshold for this diagnosis was arbitrarily set at "i2t2": a mononuclear interstitial cell infiltrate present in at least 25% of normal parenchyma and >4 mononuclear cells within the tubular basement membrane of nonatrophic tubules. TCMR was usually found in dysfunctional grafts with an elevation in the serum creatinine; however, our group and others found this extent of inflammation in "routine" or "protocol" biopsies of normally functioning grafts: "subclinical" TCMR. The prevalence of TCMR is higher in the early months posttransplant and has decreased with the increased potency of current immunosuppressive agents. However, the pathogenicity of lesser degrees of inflammation under modern immunosuppression and the relation between ongoing inflammation and development of donor-specific antibody has renewed our interest in subclinical alloreactivity. Finally, the advances in our understanding of pretransplant risk assessment, and our increasing ability to monitor patients less invasively posttransplant, promises to usher in the era of precision medicine.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.000 |
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".