Red blood cell specifications for patients with hemoglobinopathies: a systematic review and guideline
Bibliographic record
Abstract
BACKGROUND: Red blood cell (RBC) transfusions remain essential in the treatment of patients with sickle cell disease (SCD) and β-thalassemia. Alloimmunization, a well-documented complication of transfusion, increases the risk of delayed hemolytic transfusion reactions, complicates crossmatching and identifying compatible units, and delays provision of transfusions. Guidance is required to optimize the RBC product administered to these patients. STUDY DESIGN AND METHODS: An international, multidisciplinary team conducted a systematic review and developed, following the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology, recommendations to assist treating physicians and transfusion specialists in their decision to select RBCs for these patients. RESULTS: Eighteen studies (17 clinical studies and one cost-effectiveness study) were included in the systematic review. The overall quality of the studies was very low. In total, 3696 patients were included: 1680 with β-thalassemia and 2016 with SCD. CONCLUSION: The panel recommends that ABO D CcEe K-matched RBCs are selected for individuals with SCD and β-thalassemia, even in the absence of alloantibodies, to reduce the risk of alloimmunization. In patients with SCD and β-thalassemia who have developed clinically significant alloantibodies, selection of RBCs antigen negative to the alloantibody is recommended, if feasible. In these patients, selection of more extended phenotype-matched RBCs will likely reduce the risk of further alloimmunization. However, given the limited availability of extended phenotype-matched units, attention should be given to ensure that a delay in transfusion does not adversely affect patient care.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".