Hemosiderosis in chronic dialysis patients: Monitoring the response to deferasirox by quantitative hepatic magnetic resonance imaging
Bibliographic record
Abstract
INTRODUCTION: Hemosiderosis of chronic dialysis has always been a frequent phenomenon in dialysis; formerly related to blood transfusions before the advent of Erythropoiesis Stimulating Agents (ESA), it is currently in connection with the use of massive doses of injectable iron, to ensure the full therapeutic efficacy of ESA. Few studies have looked at the therapeutic aspect of iron chelators in the dialysis population. METHODS: We followed 31 dialysis patients treated for secondary hemosiderosis with deferasirox (DFX) at the dose 10 mg/kg/day, by hepatic MRI from September 2017 to September 2021, in order to evaluate the efficacy of iron chelators on the reduction of liver iron concentration (LIC). The diagnosis of hemosiderosis was carried for a value of the LIC > 50 μmol/g of dry liver. RESULTS: Chelation resulted in a significant reduction in liver iron burden as measured by liver MRI: (201.4 ± 179.9 vs. 122.6 ± 154.3 μmol/g liver) (p = 0.000) and in mean ferritin level: (2058.8 ± 2004.9 vs. 644.2 ± 456.6 ng/mL) (p = 0.002). A gain of 1.1 g/dL in mean hemoglobin level: (10.5 ± 1.6 vs. 11.6 ± 2.0 g/dL) (p = 0.006). A significant increase in mean albumin level: (43 ± 5.5 to 46.2 ± 6.1 g/L) (p = 0.04). The therapeutic response was clearly influenced by the cause of overload, longer in polytransfused patients (p = 0.023) and the degree of overload assessed by MRI (p = 0.003) and ferritin level (p = 0.04). CONCLUSION: DFX, prescribed at a dose of 10 mg/kg/day, resulted in a significant reduction in hepatic iron burden as measured by liver MRI and ferritin. The therapeutic response was clearly influenced by blood transfusions and the degree of iron overload.
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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 | 0.000 | 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".