4H Leukodystrophy: Lessons from 3T Imaging
Bibliographic record
Abstract
Abstract 4H leukodystrophy is characterized by hypomyelination, hypodontia, and hypogonadotropic hypogonadism. With its variability in clinical symptoms, application of pattern recognition to identify specific magnetic resonance imaging (MRI) features proved useful for the diagnosis. We collected 3T MR imaging data of 12 patients with mutations in POLR3A (n = 8), POLR3B (n = 3), and POLR1C (n = 1), all obtained at the same scanner. We assessed these images and compared them with previously obtained 1.5T images in 8 patients. Novel MRI findings were myelin islets, closed eye sign, and a cyst-like lesion in the splenium. Myelin islets were variable numbers of small T1 hyperintense and T2 hypointense dots, mostly in the frontal and parietal white matter, and present in all patients. This interpretation was supported with perivascular staining of myelin protein in the hypomyelinated white matter of a deceased 4H patient. All patients had better myelination of the medial lemniscus with a relatively hypointense signal of this structure on axial T2-weighted (T2W) images (“closed eye sign”). Five patients had a small cyst-like lesion in the splenium. In 10 patients with sagittal T2W images, we also found spinal cord hypomyelination. In conclusion, imaging at 3T identified additional features in 4H leukodystrophy, aiding the MRI diagnosis of this entity.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".