Effect of comorbidity on mortality in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: We aimed to compare survival in the multiple sclerosis (MS) population with a matched cohort from the general population, and to evaluate the association of comorbidity with survival in both populations. METHODS: Using population-based administrative data, we identified 5,797 persons with MS and 28,807 controls matched on sex, year of birth, and region. We estimated annual mortality rates. Using Cox proportional hazards regression, we evaluated the association between comorbidity status and mortality, stratifying by birth cohort, and adjusting for sex, socioeconomic status, and region. We compared causes of death between populations. RESULTS: Median survival from birth in the MS population was 75.9 years vs 83.4 years in the matched population. MS was associated with a 2-fold increased risk of death (adjusted hazard ratio 2.40; 95% confidence interval: 2.24-2.58). Several comorbidities were associated with increased hazard of death in both populations, including diabetes, ischemic heart disease, depression, anxiety, and chronic lung disease. The magnitude of the associations of mortality with chronic lung disease, diabetes, hypertension, and ischemic heart disease was lower in the MS population than the matched population. The most common causes of death in the MS population were diseases of the nervous system and diseases of the circulatory system. Mortality rates due to infectious diseases and diseases of the respiratory system were higher in the MS population. CONCLUSION: In the MS population, survival remained shorter than expected. Within the MS population, comorbidity was associated with increased mortality risk. However, comorbidity did not preferentially increase mortality risk in the MS population as compared with controls.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".