Extensive subpial cortical demyelination is specific to multiple sclerosis
Bibliographic record
Abstract
Cortical demyelinated lesions are frequent and widespread in chronic multiple sclerosis (MS) patients, and may contribute to disease progression. Inflammation and related oxidative stress have been proposed as central mediators of cortical damage, yet meningeal and cortical inflammation is not specific to MS, but also occurs in other diseases. The first aim of this study was to test whether cortical demyelination was specific for demyelinating CNS diseases compared to other CNS disorders with prominent meningeal and cortical inflammation. The second aim was to assess whether oxidative tissue damage was associated with the extent of neuroaxonal damage. We studied a large cohort of patients diagnosed with demyelinating CNS diseases and non-demyelinating diseases of autoimmune, infectious, neoplastic or metabolic origin affecting the meninges and the cortex. Included were patients with MS, acute disseminated encephalomyelitis (ADEM), neuromyelitis optica (NMO), viral and bacterial meningoencephalitis, progressive multifocal leukoencephalopathy (PML), subacute sclerosing panencephalitis (SSPE), carcinomatous and lymphomatous meningitis and metabolic disorders such as extrapontine myelinolysis, thus encompassing a wide range of adaptive and innate cytokine signatures. Using myelin protein immunohistochemistry, we found cortical demyelination in MS, ADEM, PML and extrapontine myelinolysis, whereby each condition showed a disease-specific histopathological pattern. Remarkably, extensive ribbon-like subpial demyelination was only observed in MS, thus providing an important pathogenetic and diagnostic cue. Cortical oxidative injury was detected in both demyelinating and non-demyelinating CNS disorders. Our data demonstrate that meningeal and cortical inflammation alone accompanied by oxidative stress are not sufficient to generate the extensive subpial cortical demyelination found in MS, but require other MS-specific factors.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".