Thalamic Atrophy As A Predictor Of Cognitive Impairment In Early Stage Of Relapsing : Remitting Multiple Sclerosis (RRMS)
Bibliographic record
Abstract
Introduction: Previous imaging and neuropathological studies have demonstrated thalamic involvement in multiple sclerosis. Cognitive deficits worsen the quality of life in multiple sclerosis and may be predicted by deep gray matter atrophy, especially in the thalamic region. This relationship has not been widely studied in the early stage of the disease. Objectives: The aims of this study were to: 1) assess cognitive deficits in patients with early stage relapsing-remitting multiple sclerosis (RRMS) using neuropsychological tests, 2) search for thalamic atrophy on brain MRI, 3) test for correlations between cognitive functions and volumetric parameters of thalamus. Methods: 60 patients (46 F, 14 M) with RRMS at an early clinical stage (median EDSS - Expanded Disability Status Scale score of 1,75) underwent neuropsychological assessment using computerized cognitive screening battery (Central Nervous System Vital Signs) and MoCa (Montreal Cognitive Assessment). Brain structures volumetry was done using automatic segmentation technique (volBrain) with 1,5T MR system acquisition. The length of the disease and the number of relapses were noted. Results: Psychomotor speed, reaction time and information processing speed were the most impaired cognitive functions in RRMS patients. For the three cognitive domains: psychomotor speed, complex attention and simple attention, thalamic area was the most sensitive MRI marker. Decreases of total thalamus volume ware associated with overall neurocognitive status of RRMS patients. Conclusions: These findings suggest that thalamic atrophy may be a clinically meaningful biomarker of cognitive decline in patients with early stage of RRMS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".