The temporal distribution of red blood cell transfusions is associated with alloimmunization risk
Bibliographic record
Abstract
BACKGROUND: Red blood cell (RBC) transfusion causes RBC alloimmunization in a st of patients. Factors that influence RBC alloimmunization risk are incompletely understood. STUDY DESIGN AND METHODS: We performed a matched case-control study of male intensive care unit (ICU) patients who did or did not develop a new RBC alloantibody after RBC transfusion. Demographic, clinical, and laboratory data were collected. Cases and controls were matched 1:2 on serological follow-up time (SFT) and the number of RBC units transfused. Conditional logistic regression analyses were performed to identify variables associated with the development of a new RBC alloantibody. RESULTS: One hundred and seventeen cases who developed a new RBC alloantibody during the SFT were matched with 234 controls who did not develop an alloantibody. The median SFT was 40 days among cases and 52 days among controls. The median number of RBC units transfused during the SFT was 7 in both groups. Although the total number of RBC units transfused was similar, cases were transfused RBC units in fewer transfusion episodes compared with controls. The median number of transfusion episodes, defined as a minimum time interval of 24 h between RBC transfusions, was higher in controls compared to cases. In multivariable analysis, each additional transfusion episode was associated with a 26% lower risk of RBC alloimmunization (odds ratio 0.74; 95% confidence interval 0.63-0.86; p < 0.001). CONCLUSIONS: In a matched case-control study of male ICU patients who received a similar number of RBC transfusions, a greater number of transfusion episodes was associated with a decreased risk of developing a new RBC alloantibody.
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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 | 0.005 |
| 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) | 0.002 | 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".