The determinants, biomarkers, and consequences of microvascular injury in kidney transplant recipients
Bibliographic record
Abstract
Independent of the initial cause of kidney disease, microvascular injury to the peritubular capillary network appears to play a central role in the development of interstitial fibrosis in both native and transplanted kidney disease. This association is explained by mechanisms such as the upregulation of profibrotic genes and epigenetic changes induced by hypoxia, capillary leakage, endothelial and pericyte transition to interstitial fibroblasts, as well as modifications in the secretome of endothelial cells. Alloimmune injury due to antibody-mediated rejection and ischemia-reperfusion injury are the two main etiologies of microvascular damage in kidney transplant recipients. The presence of circulating donor-specific anti-human leukocyte antigen (HLA) antibodies, histological findings, such as diffuse C4d staining in peritubular capillaries, and the extent and severity of peritubular capillaritis, are commonly used clinically to provide both diagnostic and prognostic information. Complement-dependent assays, circulating non-HLA antibodies, or evaluation of the microvasculature with novel imaging techniques are the subject of ongoing studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".