Measuring Demyelination and Remyelination in Acute Multiple Sclerosis Lesion Voxels
Bibliographic record
Abstract
OBJECTIVE: To validate the use of the magnetization transfer ratio (MTR) as a practical imaging marker of demyelination and remyelination in acute multiple sclerosis lesions. DESIGN: Case study. SETTING: University hospital multiple sclerosis clinic. Patients Six patients with relapsing-remitting multiple sclerosis and acute gadolinium-enhancing lesions were studied serially using a quantitative magnetization transfer examination. MAIN OUTCOME MEASURES: Changes in the water content and macromolecular content, a marker of myelin content that, unlike MTR, is not affected by changes in water content (edema) associated with acute inflammation, and changes in MTR of lesions. RESULTS: Both the macromolecular content and MTR were lower than normal in acute lesions and recovered over several months. The decrease in macromolecular content relative to contralateral normal-appearing white matter was greater than the decrease in MTR (0.46 vs 0.75 at the time of gadolinium enhancement), likely because edema in the acute lesion increased the T1 relaxation time of water and attenuated the decrease in MTR. Nevertheless, there was still a strong correlation between changes in the relative MTR and macromolecular content (R(2) = 0.70; P < .001). CONCLUSION: Our data support the use of MTR as a practical marker of demyelination and remyelination, even in acute lesions where decreases in MTR are attenuated because of the effects of edema.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".