Transplanting the highly sensitized patient
Bibliographic record
Abstract
PURPOSE OF REVIEW: Humoral sensitization to antigens of the human leukocyte antigen and ABO systems remains one of the largest barriers to further expansion in renal transplantation. This barrier translates into prolonged waiting time and a greater likelihood of death. The number of highly sensitized patients on the renal transplant waiting list continues to increase. This review focuses on the options available to these patients and speculates on future directions for incompatible transplantation. RECENT FINDINGS: Desensitization protocols (to remove antibodies), kidney-paired donation (to circumvent antibodies) or a hybrid technique involving a combination of both have broadened the access to transplantation for patients disadvantaged by immunologic barriers. However, the risk of antibody-mediated rejection may be increased and warrants caution. Technical advances in antibody characterization using sensitive bead immunoassays and the C1q assay and therapeutic modalities such as complement inhibitors and proteasome inhibitors have been used to avoid or confront these antibody incompatibilities. SUMMARY: A growing body of knowledge and literature indicates that these diagnostic and therapeutic modalities can facilitate a safer and more successful treatment course for these difficult-to-treat patients. Rigorous investigations into newer interventions will help in broadening the options for these patients and also expand the living donor pool.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".