Magnetic Resonance Imaging Findings in Neonatal Hemochromatosis
Bibliographic record
Abstract
BACKGROUND: There are limited data on utility of magnetic resonance imaging (MRI) in the assessment of suspected neonatal hemochromatosis (NH). OBJECTIVES: The aim of the study was to present our experience with utilization of multi-echo sequence MRI technique in the evaluation of NH and to compare MRI findings in infants with and without NH. METHODS: MRI performed for suspected NH were retrospectively reviewed to note the presence and severity of iron deposition (ID) in liver, spleen, pancreas, and kidneys on multi-echo sequences. Findings were compared in infants with and without NH. RESULTS: Of 20 infants (9 boys and 11 girls; median age of 12.5 days) included in the study, 7 of 20 had NH and 13 of 20 were assigned to the non-NH group. Higher degree of pancreatic ID was seen in the NH group (P = 0.001) with 4 of 7 evaluable pancreas showing moderate-to-severe degree and 1 of 7 showing mild degree of ID whereas none of the 13 infants in non-NH group showed moderate or severe degree of pancreatic ID. Even though the severity of hepatic ID was higher in NH group (P = 0.033), variable severity of hepatic ID was seen in both groups with most infants in both groups showing moderate-to-severe degree of ID. The severity of splenic ID was not particularly associated with any group (P = 0.774) but there was no moderate or severe degree of ID in NH. Renal ID was seen in two infants in non-NH group. CONCLUSIONS: A moderate-to-severe degree of pancreatic ID seen on MRI tends to be associated with NH and should be sought to establish a timely diagnosis of NH. Presence and severity of hepatic ID cannot be used for differentiation of NH from other causes of neonatal liver failure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".