Rate of D‐alloimmunization in trauma does not depend on the number of <scp>RhD</scp>‐positive units transfused: The <scp>BEST</scp> collaborative study
Bibliographic record
Abstract
BACKGROUND: Evidence indicates the life-saving benefits of early blood product transfusion in severe trauma resuscitation. Many of these products will be RhD-positive, so understanding the D-alloimmunization rate is important. METHODS: This was a multicenter, retrospective study whereby injured RhD-negative patients between 18-50 years of age who received at least one unit of RhD-positive red blood cells (RBC) or low titer group O whole blood (LTOWB) during their resuscitation between 1 January, 2010 through 31 December, 2019 were identified. If an antibody detection test was performed ≥14 days after the index RhD-positive transfusion then basic demographic information was collected, including whether the patient became D-alloimmunized. The overall D-alloimmunization rate, and the rate stratified by the number of units transfused, were calculated. RESULTS: Data were collected from nine institutions. Five institutions reported fewer than 10 eligible patients each and were excluded. From the remaining four institutions, all from the USA, there were 235 eligible patients; 77 (random effects estimate: 32.7%; 95% CI: 19.1-50.1%) became D-alloimmunized. Three of the institutions reported D-alloimmunization rates ≥38.6%, while the remaining institution's rate was 12.2%. In both random and fixed-effects models, the rate of D-alloimmunization was not significantly different between those who received one RhD-positive unit and those who received multiple RhD-positive units. CONCLUSION: In this large, multicenter study of injured patients, the overall rate of D-alloimmunization fell within the range previously reported. The rate of D-alloimmunization did not increase as the number of transfused RhD-positive units increased. These data can help to inform RhD type selection decisions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".