Comorbidity increases the risk of hospitalizations in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: We aimed to evaluate the association between comorbidity and rates of hospitalization in the multiple sclerosis (MS) population as compared to a matched cohort from the general population. METHODS: Using population-based administrative data from the Canadian province of Manitoba, we identified 4,875 persons with MS and a matched general population cohort of 24,533 persons. We identified all acute care hospitalizations in the period 2007-2011. Using general linear models, we evaluated the association between comorbidity status and hospitalization rates (all-cause, non-MS-related, MS-related) in the 2 populations, adjusting for age, sex, and socioeconomic status. RESULTS: Comorbidity was common in both cohorts. Over the 5-year study period, the MS population had a 1.5-fold higher hospitalization rate (adjusted rate ratio [aRR] 1.56; 95% confidence interval [CI] 1.44-1.68) than the matched population. Any comorbidity was associated with a 2-fold increased risk of non-MS-related hospitalization rates (aRR 2.21; 95% CI 1.73-2.82) in the MS population, but a nearly 4-fold increase in hospitalization rates in the matched population (aRR 3.85; 95% CI 3.40-4.35). Comorbidity was not associated with rates of hospitalization for MS-related reasons, regardless of how comorbidity status was defined. CONCLUSIONS: In the MS population, comorbidity is associated with an increased risk of all-cause hospitalizations, suggesting that the prevention and management of comorbidity may reduce hospitalizations.
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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 | 0.001 |
| 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) | 0.001 | 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".