Comprehensive Assessment and Standardization of Solid Phase Multiplex-Bead Arrays for the Detection of Antibodies to HLA
Bibliographic record
Abstract
Solid phase multiplex-bead arrays for the detection and characterization of HLA antibodies provide increased sensitivity and specificity compared to conventional lymphocyte-based assays. Assay variability due to inconsistencies in commercial kits and differences in standard operating procedures (SOP) hamper comparison of results between laboratories. The Clinical Trials in Organ Transplantation Antibody Core Laboratories investigated sources of assay variation and determined if reproducibility improved through utilization of SOP, common reagents and normalization algorithms. Ten commercial kits from two manufacturers were assessed in each of seven laboratories using 20 HLA reference sera. Implementation of a standardized (vs. a nonstandardized) operating procedure greatly reduced MFI variation from 62% to 25%. Although laboratory agreements exceeded 90% (R(2) ), small systematic differences were observed suggesting center specific factors still contribute to variation. MFI varied according to manufacturer, kit, bead type and lot. ROC analyses showed excellent consistency in antibody assignments between manufacturers (AUC > 0.9) and suggested optimal cutoffs from 1000 to 1500 MFI. Global normalization further reduced MFI variation to levels near 20%. Standardization and normalization of solid phase HLA antibody tests will enable comparison of data across laboratories for clinical trials and diagnostic testing.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".