Prevalence and risk factors for RBC alloantibodies in blood donors in the Recipient Epidemiology and Donor Evaluation Study‐III (REDS‐III)
Bibliographic record
Abstract
BACKGROUND: Little information exists on red blood cell (RBC) alloimmunization in healthy US blood donors, despite the potential significance for donors themselves, blood recipients, and the blood center. STUDY DESIGN AND METHODS: Donor/donation data were sourced from the Recipient Epidemiology and Donor Evaluation Study-III, which contains information from four US blood centers during 2012 through 2016. Multivariable logistic regression was used to assess prevalence of positive antibody screen by donor demographics, blood type, parity, and transfusion history. RESULTS: More than 2 million units were collected from 632,378 donors, with 0.51% of donations antibody screen positive and 0.77% of donors having at least one positive antibody screen. The most common antibody specificities were D (26.4%), E (23.8%), and K (21.6%). Regression analysis indicated that increasing age, female sex, D-negative status, and history of transfusion and pregnancy were positively associated with a positive antibody screen. Prior transfusion history was most strongly associated with a positive antibody screen, with donors reporting a prior transfusion having a higher adjusted odds ratio (3.9) of having a positive antibody screen compared to donors reporting prior pregnancy (adjusted odds ratio, 2.0). Though transfusion was a more potent immune stimulus for RBC alloantibody formation than pregnancy, the sheer number of previously pregnant donors contributed to pregnancy being a risk factor for the majority of clinically significant RBC alloantibodies detected in females. CONCLUSION: These findings on prevalence of and risk factors for RBC antibodies may have implications for future medical care of donors and for operations at blood centers.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".