Characterization of Transfusion-Derived Iron Deposition in Childhood Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: Childhood cancer survivors (CCS) receiving packed red blood cell (PRBC) transfusions may have increased risk for vital organ iron deposition causing serious late effects. METHODS: This cross-sectional cohort study of a CCS cohort quantified organ iron content by magnetic resonance imaging. Iron status by serum markers and hemochromatosis gene mutation status were assessed. RESULTS: Seventy-five patients who had received a range (0-392 mL/kg) of cumulative PRBC transfusion volumes were enrolled (median age 14 years, range 8-25.6 years at evaluation). Median follow-up time was 4.4 years, and median time since last transfusion was 4.9 years. Cancer diagnoses included acute lymphoblastic or myelogenous leukemia (ALL/AML; n = 33) and solid tumors (n = 42). Liver and pancreatic iron concentrations were elevated in 36 of 73 (49.3%) and 19 of 72 (26.4%) subjects, respectively. Cardiac iron concentration was not increased in this cohort. In multivariate analysis, cumulative PRBC volume (P < 0.0001) and older age at diagnosis (P < 0.0001) predicted elevated liver iron concentration. CONCLUSIONS: Iron overload (IO) may occur in children and adolescents/young adults treated for cancer and is associated with cumulative PRBC transfusion volume and age at diagnosis. IMPACT: These findings have implications for development of monitoring and management guidelines for cancer patients and survivors at risk for IO, exploration of the additive risk of liver/pancreatic damage from chemotherapeutic exposures, and health education to minimize further liver/pancreatic damage from exposures such as excessive alcohol intake and hepatotoxic medications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".