E.1 The role of the glymphatic system in the neurodegeneration associated to Multiple Sclerosis
Bibliographic record
Abstract
Background: Recent research highlights the glymphatic system’s role in clearing waste from the central nervous system. Its dysfunction is linked to neurodegenerative diseases like Alzheimer’s and Parkinson’s, but its impact on multiple sclerosis (MS) remains unclear. This retrospective study examines glymphatic function in MS and its link to clinical disability using MRI. Methods: The study included 18 patients diagnosed with MS, comprising 3 with primary progressive MS and 15 with secondary progressive MS, along with 7 healthy control participants. All subjects underwent neurological evaluations and MRI assessments, which included high-resolution T1, T2, and diffusion-weighted imaging. Statistical comparisons between the groups were conducted using a two-sample t-test. Results: MS patients exhibited a reduced diffusion along the perivascular space index (DTI-ALPS) compared to healthy controls. Patients with primary progressive MS demonstrated lower DTI-ALPS values than those with secondary progressive MS. Lower DTI-ALPS was associated with a higher Expanded Disability Status Scale (EDSS) score, indicating a correlation between glymphatic system dysfunction and greater clinical disability in MS. Conclusions: The study suggests that glymphatic system dysfunction is present in MS and is inversely associated with the severity of disability. This impairment may contribute to the disease’s pathological mechanisms.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".