Multiple Sclerosis: Magnetization Transfer MR Imaging of White Matter before Lesion Appearance on T2-weighted Images
Bibliographic record
Abstract
PURPOSE: To determine the evolution of magnetization transfer (MT) in white matter regions before and after plaque development in patients with multiple sclerosis (MS). MATERIALS AND METHODS: In a 5-year longitudinal evaluation, 30 patients with MS underwent conventional magnetic resonance (MR) imaging, MT MR imaging, and clinical assessment. Cross-sectional data in 12 healthy subjects were also collected. Semiautomated lesion classification with use of T2-weighted MR images was used to measure the time course of the MT ratio (calculated with MR data acquired without and with MT saturation) in every voxel and to help analyze the relationship with the status of lesions depicted on T2-weighted images. RESULTS: There was a significant (P <.001) temporal decline in lesion MT ratio after lesion appearance on T2-weighted images. A significant (P <. 001) progressive decline in MT ratio was also present in voxels that later became lesions, prior to initial detection on T2-weighted images. Even 1(1/2) years prior to lesion appearance, the MT ratio (33.3%) in regions destined to become such lesions was significantly (P <.001) lower than that in both white matter in healthy subjects (41.3%) and other normal-appearing white matter in patients with MS (38.1%). CONCLUSION: The MT ratio reveals progressive focal abnormalities in MS that antedate by up to 2 years the appearance of lesions on T2-weighted MR images.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".