Why do people still make anti‐D over 50 years after the introduction of Rho(D) immune globulin? A Biomedical Excellence for Safer Transfusion (BEST) Collaborative study
Bibliographic record
Abstract
BACKGROUND: Rho(D) immune globulin (RhIg) is used to reduce RhD alloimmunization in pregnancy. This study describes potential causes for RhD alloimmunization after the development and implementation of RhIg. STUDY DESIGN AND METHODS: This retrospective descriptive study investigated RhD-negative patients born in 1965-2005 with anti-D newly identified during 2018-2022. Transfusion, pregnancy, intravenous drug abuse, and transplantation were considered potential alloimmunization sources. RESULTS: There were 1200 study patients (852 females; 348 males) at 30 institutions in 5 countries (USA, Canada, UK, New Zealand, Brazil). Most patients had a single potential source of alloimmunization identified (857/1200, 71%), most commonly pregnancy among females (537/852, 63%) and transfusion among males (180/348, 52%). When multiple potential sources were included, males were more likely than females to have a history of transfusion (235/348 [68%] vs. 149/852 [17%], p < .0001) and confirmed or suspected intravenous drug abuse (100/348 [29%] vs. 138/852 [16%], p < .0001). Among females with a history of pregnancy, 119/718 (17%) had healthcare access issues, 120/718 (17%) had pregnancy in a country where they may not have received RhIg, and 21/718 (3%) refused RhIg. Among patients with a history of transfusion, males were more likely than females to have received RhD-positive red blood cells or whole blood (143/235 [61%] vs. 30/149 [20%], p < .0001) and/or platelets (84/235 [36%] vs. 19/149 [13%], p < .0001). DISCUSSION: Pregnancy was the most frequently identified potential source of RhD alloimmunization among females. Transfusion was most frequent in males. Intravenous drug abuse as a common potential source among patients with RhD alloimmunization merits further study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".