Effect of impairment on health-related quality of life in people with multiple sclerosis: association of functional systems and EQ-5D-5L index values in a cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Multiple sclerosis (MS) results in physical and cognitive impairments that negatively affect health-related quality of life (HRQoL). It is unknown to what extent the impact of MS-related impairments on HRQoL are reflected in the association of Expanded Disability Status Scale (EDSS) Functional Systems (FS) scores and EQ-5D-5L index values. METHODS: This cross-sectional, single-center cohort study recruited people with MS (pwMS) attending an outpatient clinic at a German university hospital. Impairment was assessed via FS scores during routine visits. HRQoL was measured with EQ-5D-5L index values. The association of each FS score with EQ-5D-5L index values and the additive effect of all FS on EQ-5D-5L index values was modeled with multivariate linear regression (MLR). RESULTS: Analyzing 115 participants, unadjusted MLR of single FS revealed that brainstem, pyramidal, cerebellar, sensory, and cerebral/cognitive dysfunctions were significantly associated with lower HRQoL. In MLR of all FS adjusted for covariates, a one standard deviation decrease in cognitive function was significantly associated with a 6% reduction in HRQoL. CONCLUSION: Dysfunctions in FS contribute to a decrease in HRQoL. Cognitive dysfunction was identified to maintain negative association with HRQoL after adjustment for covariates, and routinely assessed FS scores appeared useful indicators to identify pwMS who may benefit from comprehensive cognitive evaluations. This study adds to the growing body of evidence emphasizing the crucial role of cognitive function in HRQoL of pwMS and highlights the need for effective screening and therapeutic strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".