Cumulative impact of comorbidity on quality of life in MS
Bibliographic record
Abstract
BACKGROUND: Little is known about the impact of comorbidity on health-related quality of life (HRQOL) in multiple sclerosis (MS). We investigated the association of comorbidity and health-related HRQOL among participants in the North American Research Committee on Multiple Sclerosis (NARCOMS). MATERIALS AND METHODS: In 2006, we queried NARCOMS participants regarding physical and mental comorbidities and HRQOL, using the Short-Form 12. We summarized physical HRQOL using the aggregate Physical Component Scale (PCS-12) score and mental HRQOL using the aggregate Mental Component Scale (MCS-12) score. We assessed multivariable associations between comorbidity and HRQOL using a general linear model, adjusting for potential confounders. RESULTS: Among 8983 respondents, the mean (SD) PCS-12 was 36.9 (11.8) and MCS-12 was 45.6 (11.6). After adjustment for sociodemographic and clinical factors, participants with any physical comorbidity had a lower PCS-12 (37.2; 95% CI: 36.4-38.1) than those without any physical comorbidity (40.1; 95% CI: 39.0-41.1). As the number of physical comorbidities increased, PCS-12 scores decreased (r = -0.25; 95% CI: -0.23 to -0.27) indicating lower reported HRQOL. Participants with any mental comorbidity had a lower MCS-12 (40.7; 95% CI: 39.8-41.6) than those without any mental comorbidity (48.5; 95% CI: 47.7-49.4). CONCLUSIONS: Comorbidity is associated with reduced HRQOL in MS. Further research should evaluate whether more aggressive treatment of comorbidities improves the HRQOL of MS patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".