Red blood cell alloantibodies are associated with increased alloimmunization against human leukocyte antigens
Bibliographic record
Abstract
BACKGROUND: Alloantibodies recognizing human leukocyte antigens (HLA) can cause immune-mediated refractoriness to platelet transfusion. An association between HLA alloimmunization and red blood cell (RBC) alloimmunization has been suggested but remains uncertain. STUDY DESIGN AND METHODS: We tested for HLA alloantibodies in 660 patients with and without RBC alloantibodies. Calculated panel reactive antibody (cPRA) values were determined for HLA alloimmunized patients. Current and historical diagnoses and blood product exposure were catalogued. Variables associated with high-level HLA alloimmunization (cPRA ≥ 90%) were evaluated. RESULTS: The cohort included 366 women and 294 men with median age of 66 years (interquartile range [IQR], 53-76). The number of patients with and without RBC alloantibodies was 447 and 213, respectively. Among patients with and without RBC alloantibodies, 20.6% and 8.5% had a cPRA ≥ 90%, respectively (p < 0.0001). In univariate analyses of men and nulliparous women and previously pregnant women, the median number of RBC alloantibodies was significantly higher in patients with a cPRA ≥ 90% (p < 0.0001). The number of RBC alloantibodies remained an independent predictor of a cPRA ≥ 90% in multivariate analysis (odds ratio [OR] 1.50, 95% confidence interval [CI] 1.22-1.85). Other independent predictors of a cPRA ≥ 90% were parity (OR 1.26, 95% CI 1.08-1.47), age (OR 0.98, 95% CI 0.97-1.00), history of renal disease (OR 1.88, 95% CI 1.02-3.48), and number of non-leukoreduced products transfused (OR 1.02, 95% CI 1.00-1.04). CONCLUSIONS: RBC alloimmunization was significantly associated with HLA alloimmunization with a cPRA ≥ 90%. RBC alloimmunization status combined with specific components of the clinical history may estimate the risk of high-level HLA alloimmunization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".