Structural abnormalities in paediatric moyamoya disease revealed by clinical magnetic resonance imaging, regionally distributed relative signal intensities and volumes
Bibliographic record
Abstract
Moyamoya disease (MMD) is a rare, progressive cerebrovascular disorder, with an unknown aetiology and pathogenesis. It is characterized by steno-occlusive changes at the terminal portion of the internal carotid artery (ICA), which is accompanied by variable development of the basal collaterals called moyamoya vessels. In this study, we investigate the potential for structural T1 magnetic resonance imaging (MRI) to help characterize MMD clinically, with the help of regionally distributed relative signal intensities (RRSIs) and volumes (RRVs). These RRSIs and RRVs provide the ability to characterize aspects of regional brain development and represent an extension to existing automated biomarker extraction technologies. This study included 269 MRI examinations from MMD patients and 993 MRI examinations from neurotypical controls, with regional biomarkers compared between groups with the area under the receiver operating characteristic curve (AUC). Results demonstrate abnormal presentation of RRSIs and RRVs in the insula (15- to 20-year old cohort, left AUC: 0.74, right AUC: 0.71) and the lateral orbitofrontal region (5- to 10-year old cohort, left AUC: 0.67; 15-20 year cohort, left AUC: 0.62, right AUC: 0.65). Results indicate that RRSIs and RRVs may help in characterizing brain development, assist in the assessment of the presentation of the brains of children with MMD and help overcome standardization challenges in multiprotocol clinical MRI. Further investigation of the potential for RRSIs and RRVs in clinical imaging is warranted and supported through the release of open-source software.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".