The Relationship between Cognitive Dysfunction and Postural Stability in Multiple Sclerosis
Bibliographic record
Abstract
Background and Objectives: Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system (CNS), most commonly characterized by balance dysfunction, fatigue syndrome and cognitive impairment. The goal of our study was to determine the association between cognitive functions and static posture control. Materials and Methods: The research group consisted of 76 randomized MS patients (ICDG 35.0) hospitalized at the Neurological Rehabilitation Clinic of the Medical University of Lodz. This group was divided into three subgroups according to the cognitive assessment based on the Mini Mental State Examination (MMSE) for patients over 65 years of age and the Montreal Cognitive Assessment (MoCA) under the age of 65. Fatigue syndrome was assessed using the Fatigue Severity Scale (FSS), and postural stability using a stabilometric platform. Results: The men demonstrated poorer stabilometric platform measurements than the women. Statistically significant differences were observed between patients without dysfunction and severe cognitive impairment. The results of the stabilometric platform were found to correlate with body mass index in all three groups of patients (Spearman’s test). Conclusions: Body mass index and cognition have impact on postural stability in MS patients with moderate disability and fatigue syndrome.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".