Clinical and MRI features contributing to the clinico-radiological dissociation in a large cohort of people with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: People with Multiple Sclerosis (PwMS) often show a mismatch between disability and T2-hyperintense white matter (WM) lesion volume (LV), that in general is referred to as the clinico-radiological paradox. OBJECTIVES: This study aimed to understand how an extensive clinical, neuropsychological, and MRI analysis could better elucidate the clinico-radiological dissociation in a large cohort of PwMS. METHODS: Clinical scores, such as Expanded Disability Status Scale (EDSS), 9 Hole Peg Test (9HPT), 25-foot Walking Test (25-FWT), Paced Auditory Serial Addition Test at 3 s (PASAT3), Symbol digit Modalities Test (SDMT), demographics, and 3 T-MRI of 717 PwMS and 284 healthy subjects (HS) were downloaded from the INNI database. Considering medians of LV and EDSS scores, PwMS were divided into four groups: low LV and disability (LL/LD); high LV and low disability (HL/LD); low LV and high disability (LL/HD); high LV and disability (HL/HD). MRI measures included: volumes of gray matter (GM), WM, cerebellum, basal ganglia and thalamus, spinal cord (SC) area, and functional connectivity of resting-state networks. RESULTS: The clinico-radiological dissociation involved 36% of our sample. HL/LD showed worse SDMT scores and lower global and deep GM volumes than HS and LL/LD. LL/HD showed lower GM, thalamus, and cerebellum volumes, and SC area than HS, and lower SC area than LL/LD. CONCLUSIONS: A more extensive clinical assessment, including cognitive tests, and MRI evaluation including deep GM and SC, could better describe the real status of the disease and help clinicians in early and tailored treatment in PwMS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".