Imaging of repeated episodes of demyelination and remyelination in multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is characterized by the formation of demyelinating lesions in the white matter (WM). However, the timecourse of the evolution of healthy white matter into fully demyelinated lesions in MS is not well understood. We use a recently proposed technique to examine magnetization transfer ratio (MTR) timecourses in lesions segmented from MTR images in patients with relapsing-remitting MS (RRMS) and secondary progressive MS (SPMS). In both groups we found MTR lesions forming both in previously normal appearing WM (de novo lesions) as well as in previously lesional tissue that appears to be experiencing a second round of inflammatory demyelination (repeat lesions). Both de novo and repeat lesions exhibited significant, but incomplete MTR recovery, suggesting partial remyelination; post-lesion MTR values in de novo lesions were similar to pre-lesion values in repeat lesions. Both de novo and repeat lesions were found in subjects in relapsing-remitting and secondary progressive stages of MS, and repeat lesions appeared relatively more common in the secondary progressive phase. These observations support the hypothesis that entirely demyelinated lesions found on histopathology are the result of multiple episodes of demyelination and incomplete remyelination, and may have implications for MS treatment development efforts aimed at neuroprotection and enhancing remyelination.
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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 | 0.001 | 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".