Characteristic MR Imaging Findings of the Neonatal Brain in RASopathies
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Neuroimaging features in neonates with RASopathies are rarely reported, and to date, there are no neuroimaging studies conducted in this population. Our aim was to investigate the occurrence of supratentorial and posterior fossa abnormalities on brain MRIs of neonates with a RASopathy. MATERIALS AND METHODS: An observational case-control study of neonates with a confirmed RASopathy was conducted. The presence of an intraventricular and/or parenchymal hemorrhage and punctate white matter lesions and assessments of the splenium of the corpus callosum, gyrification of the cortical gray matter, and enlargement of the extracerebral space were noted. The vermis height, transverse cerebellar diameter, cranial base angle, tentorial angle, and infratentorial angle were measured. RESULTS: We reviewed 48 brain MR studies performed at 3 academic centers in 3 countries between 2009 and 2017. Sixteen of these infants had a genetically confirmed RASopathy (group 1), and 32 healthy infants were enrolled as the control group (group 2). An increased rate of white matter lesions, extracerebral space enlargement, simplification of the cortical gyrification, and white matter abnormalities were seen in group 1 (P < .001, for each). The vermis height of patients was significantly lower, and tentorial and infratentorial angles were significantly higher in group 1 (P = .01, P < .001, and P = .001, respectively). CONCLUSIONS: Neonates with a RASopathy had characteristic structural and acquired abnormalities in the cortical gray matter, white matter, corpus callosum, cerebellum, and posterior fossa. This study provides novel neuroimaging findings on supratentorial and posterior fossa abnormalities in neonates with a RASopathy.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".