Clinical and Magnetic Resonance Imaging (MRI) Distinctions Between Tumefactive Demyelination and Brain Tumors in Children
Bibliographic record
Abstract
Tumefactive demyelinating lesions can be difficult to distinguish from tumors. Clinical and magnetic resonance imaging features of children with tumefactive demyelination and supratentorial brain tumors were compared. Patients were identified through a 23-site national demyelinating disease study, and from a single-site neuroradiology database. For inclusion, lesions met at least 1 of 3 criteria: maximal cross-sectional diameter >20 mm, local or global cerebral mass effect, or presence of perilesional edema. Thirty-one children with tumefactive demyelination (5 with solitary lesions) were identified: 27 of 189 (14.3%) from the demyelinating disease study and 4 from the database. Thirty-three children with tumors were identified. Children with tumefactive demyelination were more likely to have an abnormal neurologic examination and polyfocal neurologic deficits compared to children with tumors. Tumefactive demyelination was distinguished from tumor by the presence of multiple lesions, absence of cortical involvement, and decrease in lesion size or detection of new lesions on serial imaging.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".