Cardiac iron overload evaluation in thalassaemic patients using T2* magnetic resonance imaging following chelation therapy: a multicentre cross-sectional study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it