Examining the effects of comorbidities on disease-modifying therapy use in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: Comorbidities are common in multiple sclerosis (MS) and adversely affect health outcomes. However, the effect of comorbidity on treatment decisions in MS remains unknown. We aimed to examine the effects of comorbidity on initiation of injectable disease-modifying therapies (DMTs) and on the choice of the initial DMT in MS. METHODS: We conducted a retrospective observational analysis using population-based health administrative and linked clinical databases in 3 Canadian provinces. MS cases were defined as any individual with ≥3 diagnostic codes for MS. Cohort entry (index date) was the first recorded demyelinating disease-related claim. The outcomes included choice of initial first-line DMTs and time to initiating a DMT. Logistic and Cox regression models were used to examine the association between comorbidity status and study outcomes, adjusting for sex, age, year of index date, and socioeconomic status. Meta-analysis was used to estimate overall effects across the 3 provinces. RESULTS: We identified 10,698 persons with incident MS, half of whom had ≥1 comorbidities. As the total number of comorbidities increased, the likelihood of initiating a DMT decreased. Comorbid anxiety and ischemic heart disease were associated with reduced initiation of a DMT. However, patients with depression were 13% more likely to initiate a DMT compared to those without depression at the index date (adjusted hazard ratio 1.13; 95% confidence interval 1.00-1.27). CONCLUSIONS: Comorbidities are associated with treatment decisions regarding DMTs in MS. A better understanding of the effects of comorbidity on effectiveness and safety of DMTs is needed.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.011 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".