Transfusional iron burden and liver toxicity after bone marrow transplantation for acute myelogenous leukemia and hemoglobinopathies
Bibliographic record
Abstract
BACKGROUND: While it is appropriate to treat transfusional iron overload to limit end-organ injury after bone marrow transplantation (BMT) for beta-thalassemia major (TM), this approach after BMT for sickle cell disease (SCD) and hematological malignancies has not been studied. PROCEDURE: Fifteen children with SCD (n = 4), TM (n = 6), or acute myelogenous leukemia (AML, n = 5) underwent HLA-identical sibling BMT between 2000 and 2003. Prospective evaluations of iron biomarkers were performed and the three groups were compared. RESULTS: The pre-BMT duration and volume of RBC transfusions varied among the three groups, but baseline ferritin and liver iron concentration (LIC) were similar. In contrast, liver histology differed. Liver inflammation was present in four TM patients and portal fibrosis was observed in five TM and one SCD patient. Hepatic veno-occlusive disease (VOD) developed in 5 of 15 patients. VOD was not associated with age, ferritin, ALT, or transfusions, but an association with liver inflammation and elevated LIC was suggested. Phlebotomy was performed in five patients after BMT. Changes in LIC were minimal in non-phlebotomized patients (P = 0.02). CONCLUSION: Iron biomarkers demonstrated significant iron overload before BMT in patients with malignant and non-malignant disorders. However, iron overload was associated with liver inflammation and VOD primarily in TM patients. The clinical significance of iron overload in patients after BMT remains uncertain, but this is the first study to suggest that VOD may be associated with transfusional iron burden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 teacher head, 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".