Impact of comorbidity on hospitalizations in individuals newly diagnosed with multiple sclerosis: A longitudinal population-based study
Bibliographic record
Abstract
BACKGROUND: It has been suggested that comorbidity in subjects with multiple sclerosis (MS) increases the risk of hospitalizations, although few studies have examined this, and rarely in an incident population. METHODS: Incident MS cases were identified retrospectively from administrative data in Saskatchewan, Canada (1996 - 2017) using a validated definition (≥3 hospital, physician or drug claims for MS); the date of the first claim for MS or a demyelinating condition was considered the index date. All hospitalizations occurring after the index were included in the analyses. Comorbidity was defined in 3 ways: any comorbidity (yes/no); a total count of comorbidity (0, 1, or ≥2); and by individual comorbidities. The impact of comorbidity on all-cause hospitalizations was examined with negative binomial regression models fitted with generalized estimating questions. In subjects with at least one hospitalization during the follow-up period, we examined associations between comorbidity and MS-related hospitalizations logistic using regression models fitted with GEE. RESULTS: Subjects with comorbidity had a higher rate of all-cause hospitalizations compared to those without any comorbidity (aRR 1.72; 95% CI: 1.48-1.99); comorbidity did not increase the odds of having an MS-specific hospitalization (aOR 0.76; 95% CI: 0.59-0.99). Individual comorbidities including diabetes, ischemic heart disease, chronic lung disease, epilepsy, and mood disorders increased the rate of all-cause hospitalizations, but had little impact on MS-related hospitalizations. A longer disease duration was associated with decreased all-cause and MS-specific admissions. CONCLUSION: Comorbidity increased the rate of all-cause, but not MS-specific, hospital admissions. Hospitalization rates were higher during the earlier stages of MS. Therefore, recognizing and managing comorbidity in the MS population, especially early in the disease course, will likely have the biggest impact on reducing overall hospital admissions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".