Transfusion‐related alloimmunization in children: epidemiology and effects of chemotherapy
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Alloimmunization rates following red blood cell (RBC) transfusion in paediatric oncology are not known. This study aimed to: (1) describe frequency and specificity of alloantibodies in paediatric oncology patients after RBC transfusions; (2) determine the effect of chemotherapy on alloimmunization rate. MATERIALS & METHODS: Retrospective cohort study of paediatric patients at a tertiary care hospital is evaluated by two groups: control group, paediatric patients without cancer; study group, paediatric oncology patients who received chemotherapy. Alloimmunization was defined as clinically significant IgG alloantibody formation against RBC antigens. RESULTS: A total of 1273 children were evaluated including 324 in study group, 909 controls, and 40 haemoglobinopathy patients. Overall, frequency of alloimmunization was 1·5%: 0·3% (95% CI: 0, 1·90) in study group; 1·3% (95% CI: 0·73, 2·32) in control group and 15% in haemoglobinopathies. The association between chemotherapy and alloimmunization was not significant; P value = 0·20 Fisher's exact test, OR 0·23 (95% CI: 0·03, 1·79). CONCLUSION: This is the first study exploring RBC alloimmunization in paediatric patients by diagnosis. Alloimmunization frequency was low. It was not possible to determine an association between chemotherapy and alloimmunization due to the low event rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".