Consequences of transfusion of platelet antibody: a case report and literature review
Bibliographic record
Abstract
BACKGROUND: Passive transfer of platelet (PLT) antibody by blood transfusion can lead to severe thrombocytopenia, bleeding, and an acute transfusion reaction. CASE REPORT: A 49-year-old male on warfarin developed thrombocytopenic bleeding within 2 hours of transfusion with a single unit of fresh-frozen plasma (FFP). The patient's PLT count on admission was 122 x 10(9) per L. Two hours after transfusion, PLT count has decreased to 5 x 10(9) per L. The patient's PLT antibody screen by solid-phase enzyme-linked immunosorbent assay was negative and his genotype was HPA-1a/1b. The donor's genotype was HPA-1b/1b and antibody screen revealed anti-HPA-1a. A lookback investigation identified another case of severe thrombocytopenia after FFP infusion 4 years previously. REVIEW OF LITERATURE: A literature review identified 19 cases of passive transfer of PLT antibody that resulted in thrombocytopenia. The PLT nadir of 7 x 10(9) per L was reached within 6 hours after transfusion with a median time to PLT recovery of 5 days. Transfusion was accompanied by an acute transfusion reaction in 30 percent of recipients. Approximately 75 percent of recipients developed thrombocytopenic bleeding. All cases involved a female donor with a history of pregnancy. High-plasma-volume components accounted for the majority of cases while anti-HPA-1a was the most frequently implicated antibody. CONCLUSION: Unexplained posttransfusion thrombocytopenia should be investigated to rule out passive transfer of PLT antibodies. Implicated donors should be deferred from subsequent donations. Switching to predominantly male plasma for transfusion may lead to reduction in cases of thrombocytopenia due to passive transfer of PLT antibody. Rational use of blood products may further reduce incidence of this complication.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".