An Insight Into the Immunologic Events and Risk Assessment in Renal Transplantation
Bibliographic record
Abstract
Organ transplantation has always been considered to be the optimal therapeutic intervention in patients with end-stage organ failure. In the US, approximately 615,000 patients are diagnosed with end-stage renal disease and less than 30% have received a kidney transplant. One of the crucial drawbacks in successful renal transplantation is allograft rejection. Survival rates among transplant recipients have greatly improved due to better understanding of transplant biology and more effective immunosuppressive agents. Post-transplant immune monitoring and optimization of the immunosuppressive therapy using non-invasive biomarkers can effectively predict impending graft rejection and may spare the need for renal biopsy. This article provides an insight into the immunomodulations of renal transplant recipients. It depicts the immune system including several types of kidney rejection and reviews the biomarkers that may serve in near future, as surveillance tools for graft monitoring. Finally, a summary on the main immunosuppressive drugs used in kidney transplant both in the induction and maintenance phases is also covered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".