Clinical Utility of Complement Dependent Assays in Kidney Transplantation
Bibliographic record
Abstract
Formation of antibodies against polymorphic HLA molecules on donor endothelium is central to the pathogenesis of antibody-mediated rejection, the dominant cause of long-term kidney allograft loss. Although introduction of the single-antigen bead assay has greatly facilitated the immune risk assessment of transplant recipients, it is recognized that not all IgG HLA antibodies detected using this method are equally relevant. In recent years, novel assays (C4d, C1q, C3d) have been developed to interrogate the complement-activating potential of anti-HLA antibodies in vitro, with the hypothesis that complement-fixing antibodies are more immediately injurious to the graft compared with noncomplement-binding antibodies. Although initial studies demonstrated the potential of these assays to risk-stratify antibodies beyond the conventional limited metric of mean fluorescence intensity values, new data from recent analyses challenge some of these early findings. In this review, we examine the technical aspects of these assays and key studies that evaluated the discriminant capacity of these tests to predict numerous outcomes in kidney transplantation. We discuss conflicting data and emerging controversies in the context of recent experimental evidence which offer new insights into the major factors that influence complement activation. Finally, we provide our perspective on the current role and utility of complement diagnostic assays as 1 variable in the multifactorial risk assessment and management of kidney transplant recipients.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".