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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".