MRI in the evaluation of pediatric multiple sclerosis
Bibliographic record
Abstract
MRI plays a pivotal role in the diagnosis of multiple sclerosis (MS) in children, as it does in adults. The presence of multiple lesions in CNS locations commonly affected by MS, along with the presence of both enhancing and nonenhancing lesions, can facilitate a diagnosis of MS at the time of a first attack, whereas the accrual of serial lesions or new clinical attacks over time confirms the diagnosis in patients not meeting such criteria at onset. T2 and enhancing lesion accrual could serve as a primary outcome metric for pediatric MS clinical trials of selected therapies with anti-inflammatory activity in order to facilitate feasible trial size numbers. More-advanced MRI techniques reveal the impact of MS on tissue integrity within both T2-bright and T1-hypointense lesions and regions of normal-appearing tissue. Volumetric MRI analyses quantify the impact of MS on age-expected brain growth, and fMRI reveals activation and resting-state functional connectivity patterns in patients with pediatric MS that differ from those seen in healthy age-matched youth. Such studies are of critical importance because MS onset during childhood may profoundly influence maturing and actively myelinating neural networks. High-field MRI visualizes MS pathology at a near-microscopic level and has the potential to more fully explain mechanisms for cognitive impairment, fatigue, and disability in patients with pediatric MS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".