Infusion hemolysis after pediatric major ABO‐mismatched bone marrow transplant: Comparison of two red blood cell depletion techniques
Bibliographic record
Abstract
BACKGROUND: During major ABO-mismatched bone marrow transplant (BMT), the infusion of incompatible red blood cells (RBCs) that are present in the bone marrow graft can cause adverse events from hemolysis. RBC depletion of the bone marrow graft can decrease this risk, but the optimal method to prevent hemolysis is unclear. PROCEDURE: We conducted a retrospective cohort study of patients who underwent major ABO-mismatched BMT at a pediatric center and had RBC depletion with either hydroxyethyl starch (HES) sedimentation or Ficoll density gradient separation. Postinfusion hemoglobinuria and creatinine values were compared. RESULTS: Between 2002 and 2016, 37 patients received HES-treated and 16 patients received Ficoll-treated major ABO-mismatched bone marrow grafts. The median residual volume of RBCs was significantly greater with HES-treated grafts (HES 21.0 ml vs. Ficoll 1.4 ml, P < 0.0001). Patients who received HES-treated grafts had a higher prevalence of postinfusion hemoglobinuria (HES 57% vs. Ficoll 6%, P = 0.0009), but renal impairment was rare. Considering only HES-treated grafts, the volume of RBCs was not associated with either postinfusion hemoglobinuria or a creatinine increase. CONCLUSIONS: Ficoll density gradient separation achieves smaller RBC volumes and less postinfusion hemoglobinuria than HES sedimentation, but both can prevent significant hemolysis. Further studies are needed to determine the residual incompatible RBC volume threshold in major ABO-mismatched BMT.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".