Magnetic resonance frequency shifts during acute MS lesion formation
Bibliographic record
Abstract
OBJECTIVE: We investigated the evolution of new multiple sclerosis (MS) lesions over time using frequency shifts of the magnetic resonance (MR) signal. METHODS: Twenty patients with relapsing-remitting MS were serially scanned for 6 months at 1-month intervals. Maps of MR frequency shifts were acquired using susceptibility-weighted imaging. New lesions were identified by enhancement with gadolinium (Gd). RESULTS: Forty new lesions were identified as areas of signal increase on Gd-enhanced scans. Up to 3 months before lesion appearance, the frequency in areas of future Gd enhancement was not detectably different from the frequency in normal-appearing white matter. Rapid increase in MR frequency was observed between 1 month before and 1 month after Gd enhancement. Two months postenhancement and later, the frequency stabilized and remained at a constantly increased level. CONCLUSIONS: These findings suggest that an increase in MR frequency does not simply reflect blood-brain barrier disruption or edema; rather, it reflects a change of tissue architecture as a consequence of new lesion formation. The data demonstrate that the MR frequency of focal MS lesions is increased before the lesions appear on conventional MRI. Unlike many other advanced imaging techniques, the images for frequency mapping can be rapidly acquired at high spatial resolution and standardized on most clinical scanners.
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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 | 0.002 |
| 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".