Adverse Impact of Comorbidity on Mortality in Multiple Sclerosis (P1.115)
Bibliographic record
Abstract
OBJECTIVE: To compare changes in survival in the MS population with those in a matched cohort from the general population, and evaluate the association of comorbidity with survival in both populations. BACKGROUND: Findings are inconsistent but some studies report that survival in MS has improved over the last 40-50 years. However, most studies suggest that survival remains lower than expected for an age and sex-matched population without MS. The reasons for this survival disparity are incompletely understood as are the relative contributions of disease-related complications and comorbidity to mortality. DESIGN/METHODS: Using provincial administrative data from 1984 through 2011 we identified the MS population in Manitoba, Canada (n = 5797) and a general population cohort matched 5:1 on sex, year of birth and region (n = 28807). We used Cox regression models to compare survival between the populations and to assess the association of comorbidity with survival adjusting for age, sex, socioeconomic status, region and birth cohort. RESULTS: The median survival from birth was 75.9 years in the MS population and 83.4 years in the matched population (p<0.0001), corresponding to a two-fold increased hazard of death (HR 2.05; 95[percnt] CI: 1.92-2.19). After adjustment, MS was still associated with an increased hazard of death (HR 2.41; 95[percnt] CI: 2.26-2.57). Diabetes, heart disease, depression, anxiety, bipolar disorder and chronic lung disease were independently associated with reduced survival, while migraine and autoimmune thyroid disease were associated with improved survival. Fifty-two percent of deaths were due to causes other than MS. CONCLUSIONS: Survival remains a median of 7 years lower than expected. At least 50[percnt] of deaths in the MS population are due to competing causes, and comorbidities are associated with an increased risk of death. Optimizing the management of comorbidity may be a means of improving survival. Study Supported by: MS Society of Canada
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".