Association between comorbidity and clinical characteristics of MS
Bibliographic record
Abstract
BACKGROUND: Comorbidity may be associated with the clinical phenotype of disease and may affect prognostication and treatment decisions. Using the North American Research Committee on Multiple Sclerosis Registry, we described comorbidities present at onset and diagnosis of multiple sclerosis (MS) and examined whether comorbidities present at onset were associated with clinical course or age of MS symptom onset. METHODS: In 2006, 8983 participants reported their physical and mental comorbidities; smoking status; height; and past and present weight. We compared clinical course at onset and age of symptom onset by comorbidity status. RESULTS: At MS onset, a substantial proportion of participants had physical (24%) or mental (8.4%) comorbidities. The mean (SD) age of MS onset was 31.2 (9.0) years. Vascular, autoimmune, cancer, visual, and musculoskeletal comorbidities were associated with a later age of symptom onset. Among men and women, the odds of a relapsing course at onset were increased if mental comorbidities (OR 1.48; 1.08-2.01) were present at symptom onset. In women, gastrointestinal comorbidities (OR 1.78; 1.25-2.52) and obesity (OR 2.08 1.53-2.82) at MS onset were also associated with a relapsing course at onset. CONCLUSIONS: Comorbidity is frequently present at onset of MS and is associated with differences in clinical characteristics.
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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 | 0.006 |
| 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 | 0.001 |
| 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".