Red‐blood‐cell alloimmunization and prophylactic antigen matching for transfusion in patients with warm autoantibodies
Bibliographic record
Abstract
BACKGROUND: Warm autoantibodies (WAA) are antibodies that react with an antigen on a patient's own red-blood-cells and can complicate compatibility testing whether or not they cause clinical haemolysis. The goal of this study was to understand the overall prevalence of WAA, the risk of RBC alloimmunization and determine whether RBC selection practices have an impact on alloimmunization. MATERIALS AND METHODS: Records of patients (>1 year of age) with an indirect antibody detection test (IAT) and serologic evidence of WAA over a 10-year-period were included. Eight centres from 5 countries collectively reviewed 1 122 245 patients who had an IAT. RESULTS: Of patients having IAT, 1214 had WAA (0·17%). Transfusion information for 1002 of the patients was available; 631 were transfused after identification of the WAA (63%); of the transfused patients, 390 received prophylactic antigen-matched (PAM) RBCs and 241 did not. Of the 372 patients with WAA who were transfused and had serologic testing 30+ days following transfusion (30-2765 days), 56 developed new RBC alloimmunization (15·1%). Patients who were transfused using a PAM strategy were not protected from new RBC alloimmunization [14·6% (31 of 212 patients) having PAM transfusion approach compared with those not receiving PAM approach (15·6%, 25 of 160 patients, P = 0·8837)]. CONCLUSIONS: The prevalence of WAA in patients having an IAT is low (<1%). A significant portion of patients with WAA form new RBC alloimmunization (15·1%); however, the use of PAM approach for RBC selection was not found to be protective against new 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 | 0.004 |
| 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) | 0.001 | 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".