Substantial Declines in Rates of Hospitalization in Multiple Sclerosis: A Population-Based Study (P4.151)
Bibliographic record
Abstract
OBJECTIVE: We aimed to evaluate temporal trends in hospitalizations in the multiple sclerosis (MS) population as compared to the general population. BACKGROUND: Substantial changes in the diagnosis and treatment of MS have occurred over the last twenty years. However, relatively little information is available regarding changes in health care utilization in the MS population over time. DESIGN/METHODS: Using population-based provincial administrative data from 1984 through 2011 we identified the MS population in Manitoba, Canada (n = 5797) and a cohort from the general population matched 5:1 on sex, year of birth and region (n = 28807). We determined the annual hospitalization rates, and used Poisson regression models with generalized estimating equations to estimate temporal changes in hospitalization rates. RESULTS: In 1984, 24.5% of the MS population was hospitalized, representing 35.0 hospitalizations per 100 person-years. By comparison, the hospitalization rate was three-fold lower in matched controls (RR 3.33; 95%CI: 1.67-6.64). Only 7.5% of matched controls were hospitalized, representing 10.5 hospitalizations per 100 person-years. The annual rate of hospitalizations declined in the MS population by 0.80 (95%CI: -0.93, -0.67) per year. Thus in 2004, the hospitalization rate was 72% lower at 9.7 than twenty years earlier. The rate of hospitalizations declined more slowly in the matched cohort (0.20; 95%CI: -0.23, -0.17), such that the hospitalization rate of 6.19 in 2004 was only 41% lower than in 1984. In the MS population, considering all 14151 hospitalizations over the study period, 20.6% were MS-related, 4.6% were possibly MS-related, and 74.8% were not MS-related. The annual proportion of admissions for MS-related reasons decreased from 43.4% in 1984 to 7.8% in 2011. Temporal trends persisted after adjusting for age, sex and socioeconomic status. CONCLUSIONS: The annual rate of hospitalizations declined dramatically in the MS population over time, exceeding declines in hospitalization rates observed in the matched cohort. A lower proportion of hospitalizations are MS-related than twenty-five years earlier. 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".