Cardiac iron overload evaluation in thalassaemic patients using T2* magnetic resonance imaging following chelation therapy: a multicentre cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Magnetic resonance imaging (MRI) T2* technique is used to assess iron overload in the heart, liver and pancreas of thalassaemic patients. Optimal iron chelation and expected tissue iron response rates remain under investigation. The objective of this study was to analyse serum ferritin and the iron concentration in the heart, liver and pancreas measured by MRI T2*/R2* during regular chelation therapy in a real-world cohort of patients with thalassemia. METHODS: We evaluated thalassaemic patients ≥ 7 years old undergoing chelation/transfusion therapy by MRI and assessed serum ferritin at baseline and follow-up from 2004-2011. RESULTS: We evaluated 136 patients, 92% major thalassaemic, with a median age of 18 years, and median baseline ferritin 2.033ng/ml (range: 59-14,123). Iron overload distribution was: liver (99%), pancreas (74%) and heart (36%). After a median of 1.2 years of follow-up, the iron overload in the myocardium reduced from 2,63 Fe mg/g to 2,05 (p 0.003). The optimal R2* pancreas cut-off was 148 Hertz, achieving 78% sensitivity and 73% specificity. However, when combining the R2* pancreas cut off ≤ 50 Hertz and a ferritin ≤ 1222 ng/ml, we could reach a negative predictive value (NPV) of 98% for cardiac siderosis. Only 28% were undergoing combined chelation at baseline assessment, which increased up to 50% on follow up evaluation. CONCLUSIONS: Chelation therapy significantly reduced cardiac siderosis in thalassaemic patients. In patients with moderate/severe liver iron concentration undergoing chelation therapy, ferritin levels and myocardium iron improved earlier than the liver siderosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".