Bibliographic record
Abstract
Despite its long-standing status as the diagnostic "gold standard", the renal transplant biopsy is limited by a fundamental dependence on descriptive, empirically-derived consensus classification. The recent shift towards personalized medicine has resulted in an increased demand for precise, mechanism-based diagnoses, which is not fully met by the contemporary transplantation pathology standard of care. The expectation is that molecular techniques will provide novel pathogenetic insights that will allow for the identification of more accurate diagnostic, prognostic, and therapeutic targets. Here we review the current state of molecular renal transplantation pathology. Despite significant research activity and progress within the field, routine adoption of clinical molecular testing has not yet been achieved. The recent development of novel molecular platforms suitable for use with formalin-fixed paraffin-embedded tissue will offer potential solution for the major barriers to implementation. The recent incorporation of molecular diagnostic criteria into the 2013 Banff classification is a reflection of progress made and future directions in the area of molecular transplantation pathology. Transcripts related to endothelial injury and NK cell activation have consistently been shown to be associated with antibody-mediated rejection. Prospective multicenter validation and implementation of molecular diagnostics for major entities remains an unmet clinical need in transplantation. It is expected that an integrated system of transplantation pathology diagnosis comprising molecular, morphological, serological, and clinical variables will ultimately provide the greatest diagnostic precision.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".