Comorbidity is associated with disease activity in MS
Bibliographic record
Abstract
OBJECTIVE: To determine whether comorbidity is associated with clinical (relapses, disability worsening) and MRI outcomes in multiple sclerosis (MS) by conducting a secondary analysis of the CombiRx clinical trial. METHODS: CombiRx compared interferon beta-1a, glatiramer acetate, and the combination of these agents. For participants eligible for evaluation of 6-month confirmed disability worsening, we used medical history, concomitant medications, and adverse events to ascertain comorbidity status. Comorbid conditions evaluated included hypertension, dyslipidemia, diabetes mellitus, depression, anxiety disorders, and migraine. Clinical outcomes included disease activity consisting of protocol-defined relapses, disability worsening, and MRI activity. We summarized the prevalence of these comorbid conditions and their association with disease activity and its components using multivariable Cox regression. RESULTS: Of the 1,008 participants randomized, 959 (95.1%) were eligible for assessment of 6-month disability worsening; for this subgroup, the median length of follow-up was 3.4 years (range 0.5-6.9 years). Overall, 55.1% of participants had ≥1 comorbidity at enrollment. After adjustment, anxiety (hazard ratio [HR] 1.25, 95% confidence interval [CI] 1.01-1.55) and dyslipidemia (HR 1.32, 95% CI 1.01-1.72) were associated with an increased hazard of any disease activity, while migraine (HR 0.80, 95% CI 0.67-0.97) was associated with a decreased hazard. CONCLUSIONS: In this large trial population with rigorously obtained outcomes, comorbid conditions were common among participants and influenced disease outcomes, including relapses. The comorbidity burden of clinical trial participants with MS may be an important factor in the outcome of clinical trials. Additional investigations of the impact of comorbidity on clinical trial outcomes and response to disease-modifying therapies are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| 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 | 0.000 |
| 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".