Factors Associated with Health-Related Quality of Life in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Much research has gone into the assessment of function and health-related quality of life (HRQOL) in those with multiple sclerosis (MS). The Medical Outcomes Study 36-item short form (SF-36) has been widely used in this population but current recommendations are that it be supplemented with condition-specific measures such as the MS Quality of Life Inventory (MSQLI) and the MS Functional Composite (MSFC). The goal of the baseline component of this study was the measurement of generic and condition-specific HRQOL, and the identification of factors associated with these outcomes. METHODS: HRQOL was assessed at the baseline phase of a longitudinal study. Participants completed the assessment during their regularly scheduled clinic visit. RESULTS: 300 of 387 eligible patients agreed to participate, for a response rate of 77.5%. Age ranged from 22 to 77 years, while duration of MS ranged from 1 to 47 years. Mean SF-36 scores were well below age- and sex-adjusted normative data. Only 240 completed the MSFC component. Higher EDSS, use of support services, pain medications, clinical depression and antidepressant use were associated with poorer HRQOL, while higher income and education were associated with better HRQOL. CONCLUSIONS: There is a substantial burden of illness associated with MS when compared to normative HRQOL data. This was more pronounced in physically- than in mentally-oriented domains. Assessment of HRQOL provides a valuable complement to the EDSS by providing information about the patient perception of function and HRQOL beyond that which can be obtained by physical assessment alone.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".