Transfusing children with sickle cell disease using blood group genotyping when the pool of Black donors is limited
Bibliographic record
Abstract
BACKGROUND: Red blood cell transfusion is an effective treatment for patients with sickle cell disease (SCD). Alloimmunization can occur after a single transfusion, limiting further usage of blood transfusion. It is recommended to match for the ABO, D, C, E, and K antigens to reduce risks of alloimmunization. However, availability of compatible blood units can be challenging for blood providers with a limited number of Black donors. STUDY DESIGN AND METHODS: A prospective cohort of 205 pediatric patients with SCD was genotyped for the RH and FY genes. Transfusion and alloimmunization history were collected. Our capacity to find RhCE-matched donors was evaluated using a database of genotyped donors. RESULTS: Nearly 9.8% of patients carried a partial D variant and 5.9% were D-. Only 45.9% of RHCE alleles were normal, with the majority of variants affecting the RH5 (e) antigen. We found an alloimmunization prevalence of 20.7% and a Rh alloimmunization prevalence of 7.1%. Since Black donors represented only 1.40% of all blood donors in our province, D- Caucasian donors were mostly used to provide phenotype matched products. Compatible blood for patients with rare Rh variants was found only in Black donors. A donor with compatible RhCE could be identified for all patients. CONCLUSION: Although Rh-compatible donors were identified, blood units might not be available when needed and/or the extended phenotype or ABO group might not match the patient. A greater effort has to be made for the recruitment of Black donors to accommodate patients with SCD.
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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 | 0.002 |
| 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 | 0.000 | 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".