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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".