Bibliographic record
Abstract
PURPOSE OF REVIEW: In the last decade, high-throughput molecular screening methods have revolutionized the transplantation research. This article reviews the new knowledge that has emerged from transplant patient sample-derived 'omics data by examining the interface between molecular signals and allograft pathology. RECENT FINDINGS: State-of-the-art molecular studies have shed light on the biology of organ transplant diseases and provided several potential molecular tests with diagnostic, prognostic, and theranostic applications for the implementation of personalized medicine in transplantation. By comprehensive molecular profiling of patient samples, we have learned numerous new insights into the effector mechanisms and parenchymal response during allograft diseases. It has become evident that molecular profiles are coordinated and move in patterns similar to histopathology lesions, and therefore lack qualitative specificity. However, molecular tests can empower precision diagnosis and prognostication through their objective and quantitative manner when they are integrated in a holistic approach with histopathology and clinical factors of patients. SUMMARY: Despite clever science and large amounts of public money invested in transplant 'omics studies, multiparametric molecular testing has not yet been translated to patient care. There are serious challenges in the implementation of transplant molecular diagnostics that have increased frustration in transplant community. We appeal for a full collaboration between pathologists and researchers to accelerate transition from research to clinical practice in transplantation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".