Cognition and Health-related Quality of Life in Multiple Sclerosis
Bibliographic record
Abstract
Background: Cognitive dysfunctions are considered as a poor prognostic factor that influence health-related quality of life in multiple sclerosis. Objective: The aim of the study was to evaluate the impact of cognitive impairment on the quality of life 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. Cognitivefunction was evaluated by the Montreal Cognitive Assessment (MoCa) screening test. Quality of life was evaluated by SF36 questionnaire. Results: 88.33% of patients had cognitive impairment with 68.33% with mild cognitive impairment. Abstraction (60,83%), language (56,66%), executive functions (53.66%) and delayed recall (28.33%) were rated the worst. The median value of SF-36 score was 54.1 (27.7-70.01). The lowest results were achieved in the QOL domains of psycial limitation with a median value of 12.5 (0-75) and emotional limitation 33.3 (0-100). It is found statistically significant correlation of the MoCa score with social functioning, energy, vitality and general health (p<0.05) and physical functioning (p<0.001) domains of quality of life, as well as with SF -36 total scores (p<0.05). Among group of patients with cognitive impairment, statistically significant positive correlation between cognitive status mental health HRQOL domain (rho=0.427; p<0.001) was found. Conclusion: Cognitive impairment is very often presented in patients with multiple sclerosis with significant contribution to a poorer quality of life. It is associated with physical and emotional limitations, as well as poorer mental health. Further studies are needed, especially when we take into account very important clinical and prognostic role of cognition in multiple sclerosis.
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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 | 0.002 |
| 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".