Dramatically changing rates and reasons for hospitalization in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: We aimed to describe hospitalizations in the multiple sclerosis (MS) population, and to evaluate temporal trends in hospitalizations in the MS population compared to the general population. METHODS: Using population-based administrative data, we identified 5,797 persons with MS and a matched general population cohort of 28,769 persons. Using general linear models, we evaluated temporal trends in hospitalization rates and length of stay in the 2 populations over the period 1984-2011. RESULTS: In 1984 the hospitalization rate was 35 per 100 person-years in the MS population and 10.5 in the matched population (relative risk [RR] 3.33; 95% confidence interval: 1.67-6.64). Over the study period hospitalizations declined 75% in the MS population but only 41% in the matched population. The proportion of hospitalizations due to MS declined substantially from 43.4% in 1984 to 7.8% in 2011. The 3 most common non-MS-related reasons for admission in the MS population were diseases of the digestive, genitourinary, and circulatory systems. Admissions for bacterial pneumonia, influenza, urinary tract infections, and pressure ulcers occurred more often in the MS population than in the general population, while admissions for circulatory system disease and neoplasms occurred less often. Older age, male sex, and lower socioeconomic status were associated with increased hospitalization rates for non-MS-related reasons. CONCLUSIONS: Although hospitalization rates have declined dramatically in the MS population over the last quarter century, they remain higher than in the general population. Admissions for MS-related reasons now constitute only a small proportion of the reasons for hospitalization.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".