Characterization of pediatric transfusion‐dependent thalassemia patients in a large academic center
Bibliographic record
Abstract
BACKGROUND: Transfusion-dependent thalassemia patients are at high risk of transfusion-related complications. Yet, there is scanty data on the frequency of transfusion reactions, particularity alloimmunization among pediatric transfusion-dependent thalassemia patients. In addition, there is no consensus on the prophylactic antigen matching for prevention of alloimmunization or the extent of antigen matching for alloimmunized thalassemia patients. METHODS: We conducted a retrospective study to assess the frequency and specificity of alloimmunization among pediatric transfusion-dependent thalassemia patients receiving ABO, RhD, and K-matched red blood cell units. In addition, we studied the association between patients' characteristics and alloimmunization. The clinical and transfusion records of transfusion-dependent thalassemia patients followed up at our institution between July 2018 and June 2022 were reviewed. RESULTS: Ninety-two transfusion-dependent thalassemia patients having mean age of 13.37 years (SD, 5.56) were included in our study. Eight patients (9%) had developed clinically significant alloantibodies; six patients (6%) developed alloantibody against E antigen while two patients (2%) developed more than one alloantibody. Of alloimmunized patients, five patients had received transfusion outside Canada. Patients' sex, age, having a genotype variant, total number, and duration of transfusion received were not associated with the risk of alloimmunization. The transfusion-recipient's diagnosis of β-thalassemia, having developed autoantibody, and history of receiving transfusion outside Canada were associated with alloimmunization. CONCLUSION: Blood matching for ABO, RhD, and K antigens resulted in, although not eliminated, lower frequency of alloimmunization than that previously reported among pediatric thalassemia patients. Extending matching to include Rh antigens could further reduce the rate of alloimmunization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".