Measuring alloantibodies: a matter of quantity and quality
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review describes the utility and limitations of measure for assessing the presence, relative strength, and clinical impact of human leukocyte antigen (HLA) alloantibodies, as well as the other qualitative features of antibodies that are important considerations in assessing patient risk. RECENT FINDINGS: Using MFI as a measure of antibody amount is limited for a variety of reasons. Standardized serum manipulations such as ethylene-diamine-tetra-acetic acid treatment or serum dilution results in better definition of relationships between MFI and antibody titer or complement activation, toward greater alignment in defining positivity. Increased understanding of HLA epitopes has improved the ability to precisely define donor specific HLA antibody (DSA) specificities and the analysis of structural HLA Class II epitope mismatches in donor-recipient pairs may assist in the prevention of de novo DSA development. Studies of antibody isotypes and immunopathological mechanisms underlying graft injury mediated by non-HLA antibodies are expanding the assessemnt of immunological risk. SUMMARY: Careful analysis of both semiquantitative and qualitative properties of donor-specific antibodies continues to improve our ability to study the effects of DSA on clinical outcomes in solid organ transplantation.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".