Management of a patient with sickle cell disease and multiple red blood cell alloantibodies in preparation for a hematopoietic stem cell transplantation
Bibliographic record
Abstract
BACKGROUND: Hematopoietic stem cell transplant (HSCT) is currently the only widely available curative option for patients with sickle cell disease (SCD). Alloimmunization in this population is frequent and can complicate transfusion management during the HSCT period. The case of a pediatric patient with severe SCD clinical phenotype, multiple alloantibodies (9), and hyperhemolysis syndrome who underwent haploidentical HSCT is described. STUDY DESIGN AND METHODS: The patient was known for an anti-e, despite RHCE*01.01 allele, which predicts a C- c+ E- weak e+ phenotype. Donors matching the patient's extended phenotype were targeted for RHCE genotyping. RESULTS: Donors homozygotes or heterozygotes for RHCE*01.01 were selected for compatibility analyses and ranked based on strength of reactions. Discordance between zygosity and strength of reactions was observed, as the most compatible donors were heterozygotes for RHCE*01.01. In total, the patient received seven RBC units from two different donors during HSCT process without transfusion reaction or development of new alloantibodies. Six months post-HSCT, his hemoglobin level is stable at around 120 g/L and his chimerism is 100%. DISCUSSION: This case highlights the complexity of transfusion management during HSCT of alloimmunized patients with SCD. Collecting sufficient compatible units requires early involvement of transfusion medicine teams and close communication with the local blood provider. Genotyping of donors self-identifying as Black is useful for identifying compatible blood for those patients but has some limitations. HSCT for heavily alloimmunized patients is feasible and safe with early involvement of transfusion medicine specialists. Further research on the clinical impact of genotypic matching is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".