Influence of Sociodemographic Characteristics on cognitive Functions in Multiple Sclerosis Patients
Bibliographic record
Abstract
Background: Multiple sclerosis is a a complex diesase that may be presented by different neurological symptoms causing impairment of physical, psychological and cognitive functions. Objective: The aim of the study was to evaluate the influence of sociodemographic characteristics on cognitive functions in multiple sclerosis patients. Methods: This study included 60 MS patients treated at the Department of Neurology, Clinical Center University of Sarajevo. Inclusion criteria were clinically definite diagnosis of multiple sclerosis, 18 years of age or older and were able to give written informed consent. Cognitive function was evaluated by the Montreal Cognitive Assessment (MoCa) screening test. Mann-Whitney and Kruskal-Wallis test were used for comparisons between sociodemographic characteristics and MoCa test scores. Results: 76.66% were female patients. Average age of patients was 44.5 years. 70% of patients were married. 73,33% of patients had a high school degree, 20% had a college degree while only 6,66% had primary education. 38,33% of patients were employed, 33,33% were unemployed and 28,33% retired. 88.33% of patients had cognitive impairment, 68.33% having mild cognitive impairment. Executive functions (53,66%) and delayed recall (28,33%) were rated the worst. The median value of the Naming and Language MoCa domains of cognition showed statistical significant correlation with level of education (p<0.05; p<0.01).The mean value of the Language variable was statistically significantly lower in respondents aged 35 and over compared to respondents younger than 35 years (p=0,003;p<0,01), Statistically significant correlation was found between the level of education and cognitive status (rho=0,276,p<0,05), while the other variables (gender, age, marital status and employment ) did not show a statistically significant corellation. Conclusion: High perecentage of MS patients has cognitive impairment. Executive functions are rated the worst. Education is the major factor that contribute to better cognitive functioning in MS patients independent of age or employment status. The highest correlation is found between language and naming domains of cognition. Gender did not prove to be predictive factor of cognition in multiple sclerosis patients at any domain.
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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 | 0.003 |
| 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) | 0.002 | 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".