The evanescence and persistence of RBC alloantibodies in blood donors
Bibliographic record
Abstract
BACKGROUND Blood donors represent a healthy population, whose red blood cell (RBC) alloantibody persistence or evanescence kinetics may differ from those of immunocompromised patients. A better understanding of the biologic factors impacting antibody persistence is warranted, as the presence of alloantibodies may impact donor health and the fate of the donated blood product. METHODS Donor/donation data collected from four US blood centers from 2012 to 2016 as part of the Recipient Epidemiology and Donor Evaluation Study‐III (REDS‐III) were analyzed. Clinically significant antibodies from blood donors with more than one donation who underwent at least one follow‐up antibody screen after the initial antibody identification were included. Of 632,378 blood donors, 481 (128 males and 353 females) fit inclusion criteria. RESULTS Antibody screens detected 562 alloantibodies, with 368 of 562 (65%) of antibodies being persistently detected and with 194 of 562 (35%) becoming evanescent. Factors associated with antibody persistence included antibody specificity, detection at the first donation, reported history of transfusion, and detection of multiple antibodies concurrently. Anti‐D, C, and Fya were most likely to persist, while anti‐M, Jka, and S were most frequently evanescent. CONCLUSIONS These data provide insight into variables impacting the duration of antibody detection, and they may also influence blood donor center policies regarding donor recruitment/acceptance.
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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 | 0.005 |
| 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 | 0.000 |
| 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".