What Have We Learned from Perfusion MRI in Multiple Sclerosis?
Bibliographic record
Abstract
Using MR imaging, perfusion can be assessed either by dynamic susceptibility contrast MR imaging or arterial spin-labeling. Alterations of cerebral perfusion have repeatedly been described in multiple sclerosis compared with healthy controls. Acute lesions exhibit relative hyperperfusion in comparison with normal-appearing white matter, a finding mostly attributed to inflammation in this stage of lesion development. In contrast, normal-appearing white and gray matter of patients with MS has been mostly found to be hypoperfused compared with controls, and correlations with cognitive impairment as well as fatigue in multiple sclerosis have been described. Mitochondrial failure, axonal degeneration, and vascular dysfunction have been hypothesized to underlie the perfusion MR imaging findings. Clinically, perfusion MR imaging could allow earlier detection of the acute focal inflammatory changes underlying relapses and new lesions, and could constitute a marker for cognitive dysfunction in MS. Nevertheless, the clinical relevance and pathogenesis of the brain perfusion changes in MS remain to be clarified.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".