Role of MRI in the differentiation of ADEM from MS in children
Bibliographic record
Abstract
BACKGROUND: Acute disseminated encephalomyelitis (ADEM) is typically a monophasic demyelinating disorder. However, a clinical presentation consistent with ADEM can also be the first manifestation of multiple sclerosis (MS), particularly in children. Quantitative analyses of MRI images from children with monophasic ADEM have yet to be compared with those from children with MS, and MRI criteria capable of distinguishing ADEM from MS at onset have yet to be derived. METHODS: A retrospective analysis of MRI scans obtained at first attack from 28 children subsequently diagnosed with MS and 20 children with ADEM was performed. T2/fluid-attenuated inversion recovery hyperintense lesions were quantified and categorized according to location, description, and size. T1-weighted images before and after administration of gadolinium were evaluated for the presence of black holes and for gadolinium enhancement. Mean lesion counts and qualitative features were compared between groups and analyzed to create a proposed diagnostic model. RESULTS: Total lesion number did not differentiate ADEM from MS, but periventricular lesions were more frequent in children with MS. Combined quantitative and qualitative analyses led to the following criteria to distinguish MS from ADEM: any two of 1) absence of a diffuse bilateral lesion pattern, 2) presence of black holes, and 3) presence of two or more periventricular lesions. Using these criteria, MS patients at first attack could be distinguished from monophasic ADEM patients with an 81% sensitivity and a 95% specificity. CONCLUSIONS: MRI diagnostic criteria are proposed that may be useful in differentiating children experiencing the first attack of multiple sclerosis from those with monophasic acute disseminated encephalomyelitis.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".