Non-conventional MRI techniques for measuring neuroprotection, repair and plasticity in multiple sclerosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize recent developments using non-conventional MRI techniques to measure neuroprotection, repair and plasticity in multiple sclerosis. RECENT FINDINGS: Recent advances in our understanding of the pathogenesis of multiple sclerosis, particularly as it relates to the development of chronic disability, have led away from a 'lesion-centric' view of multiple sclerosis towards investigating neurodegeneration and pathology in normal appearing brain tissue. Advanced image processing techniques that measure atrophy globally and regionally also have provided insight into the putative mechanisms that contribute to neurodegeneration. In addition, novel quantitative imaging techniques that are more specific than conventional MRI for myelin and axonal pathology have been instrumental in revealing the dynamic nature of injury and repair of myelin and axons in lesions. Novel imaging techniques that are sensitive to the pathology of myelin and axons that happens in multiple sclerosis also provide a method by which we can measure neuroprotection and test the efficacy of putative therapeutic agents in multiple sclerosis. SUMMARY: Non-conventional MRI techniques have contributed to a greater understanding of the complex pathogenesis of neurodegenerative phenomena that occur in multiple sclerosis. The pathological specificity of these novel imaging methods enables the evaluation of the neuroprotective effects of novel therapeutic strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".