Iron overload in Brazilian thalassemic patients
Bibliographic record
Abstract
UNLABELLED: ABSTRACTObjectives:To evaluate the use of magnetic resonance imaging in patients with β-thalassemia and to compare T2* magnetic resonance imaging results with serum ferritin levels and the redox active fraction of labile plasma iron. METHODS: We have retrospectively evaluated 115 chronically transfused patients (65 women). We tested serum ferritin with chemiluminescence, fraction of labile plasma iron by cellular fluorescence and used T2* MRI to assess iron content in the heart, liver, and pancreas. Hepatic iron concentration was determined in liver biopsies of 11 patients and the results were compared with liver T2* magnetic resonance imaging. RESULTS: The mean serum ferritin was 2,676.5 +/- 2,051.7 ng/mL. A fraction of labile plasma iron was abnormal (> 0,6 Units/mL) in 48/83 patients (57%). The mean liver T2* value was 3.91 ± 3.95 ms, suggesting liver siderosis in most patients (92.1%). The mean myocardial T2* value was 24.96 ± 14.17 ms and the incidence of cardiac siderosis (T2* < 20 ms) was 36%, of which 19% (22/115) were severe cases (T2* < 10 ms). The mean pancreas T2* value was 11.12 ± 11.20 ms, and 83.5% of patients had pancreatic iron deposition (T2* < 21 ms). There was significant curvilinear and inverse correlation between liver T2* magnetic resonance imaging and hepatic iron concentration (r= -0.878; p < 0.001) and moderate correlation between pancreas and myocardial T2* MRI (r = 0.546; p < 0.0001). CONCLUSION: A high rate of hepatic, pancreatic and cardiac impairment by iron overload was demonstrated. Ferritin levels could not predict liver, heart or pancreas iron overload as measured by T2* magnetic resonance imaging. There was no correlation between liver, pancreas, liver and myocardial iron overload, neither between ferritin and fraction of labile plasma iron with liver, heart and pancreas T2* values.
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.
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) | 0.002 | 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".