MRI measures of brain injury in children with Multiple Sclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multiple sclerosis (MS) is thought to be an autoimmune disease that affects the central nervous system of young adults.Although an uncommon disease in children, recent research has examined the effect of this disease upon a younger demographic group.This patient population has attracted the attention of MS researchers, as it promises a better understanding of the pathophysiology of MS at its earliest stage.Magnetic resonance imaging (MRI), a sensitive tool for detecting white matter (WM) pathology, has improved the diagnosis and appreciation of the pathogenesis of MS in adults.However, little is known about its use in children.Therefore, the main objective of this thesis is to contribute and to increase knowledge in this important new area.For this purpose, specific image processing methodologies were developed, and pathology on MRI was compared between patients with adult-and pediatric-onset MS.Comparing the spatial distribution, frequency, and volume of lesions on T2-weighted (T2w) MR images among patients with pediatric-and adult-onset MS, who had similar disease duration, showed a similar total T2w lesions between the two groups.However, children exhibited a higher T2w lesion volume and frequency in the infratentorial region, particularly in the pontine region.Persistent T1-weighted (T1w) lesions, a marker of permanent tissue damage and axonal loss, were assessed to determine whether MS lesions in children are as destructive as those in adults.To obtain a fair comparison using the scans available, normalization of intensity was essential.We showed the limitations of the currently available techniques
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it