Validation of R2* magnetic resonance imaging for quantifying secondary iron overload in pediatric patients
Bibliographic record
Abstract
Validation of R2* magnetic resonance imaging for quantifying secondary iron overload in pediatric patients PURPOSE Non-invasive assessment of iron deposition is the standard of care for guiding chelation therapy in patients with iron overload.Several magnetic resonance imaging (MRI)-based techniques have been developed.This study compares the MRI-based R2* method with the standard R2-based method for quantifying iron levels in the liver and heart in children and young adults with secondary iron overload. METHODSA single-center prospective study was conducted over 2.5 years involving 14 patients aged 4-22 years with secondary iron overload.These patients underwent 40 MRI scans using both R2 and R2* methods at same time.A total of 36 scans were analyzed, comparing the two methods using linear regression analysis and Bland-Altman plots. RESULTSThe study shows a significant correlation between liver iron concentration measurements obtained using the R2* method and those obtained using the R2-based method (adjusted R 2 = 0.77128).The agreement was even stronger for R2* values in the cardiac septum (adjusted R 2 = 0.93483). CONCLUSIONThe R2* method for assessing iron deposition in the liver and cardiac septum is comparable to the R2-based method and is suitable for clinical use.However, due to slight differences in measurements between the two techniques, it is advisable to consistently use one method for monitoring treatment in each patient.Further research is needed to refine the calibration equations. CLINICAL SIGNIFICANCEThis study highlights the MRI-based R2* method as a reliable, non-invasive, and cost-effective alternative to the R2-based method for monitoring iron overload in pediatric patients, with no additional costs for institutions or third parties.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".