Hospital admissions and MS: temporal trends and patient characteristics.
Bibliographic record
Abstract
OBJECTIVES: Hospital admissions are important surrogate measures for disease worsening and increased demand on healthcare resources; few studies have examined hospitalizations in multiple sclerosis (MS). We examined hospital admission rates and patterns in a large Canadian MS cohort. STUDY DESIGN: Retrospective, observational study. METHODS: Data from the British Columbia MS database were linked with hospital separation and registry administrative data, 1986 to 2008. Main outcomes included all-cause hospital admission rates and length of stay. The influence of time and patient characteristics was examined using multivariable regression models. RESULTS: Overall rate of all-cause admissions was 32.4 per 100 MS patients. Rates decreased by 1.4% (adjusted incidence rate ratio [IRR] 0.986; 95% confidence interval [CI] 0.982-0.990) per year from 1986 onward. Higher admission rates were associated with older age (adjusted IRR 1.011; 95% CI 1.007-1.014), primary progressive MS (adjusted IRR 1.294; 95% CI 1.162-1.441), and a longer disease duration (adjusted IRR 1.030; 95% CI 1.027-1.034). Mean length of inpatient stay was 10.2 (standard deviation [SD] 24.8) days, and increased over time. Hospital stays were longer for older patients and those with a longer disease duration, but were not influenced by sex or disease course. CONCLUSIONS: Admission rates for MS patients have decreased since 1986, but length of stay has increased. Patients with a longer disease duration and those with primary progressive MS had higher rates of admission and longer stays. Understanding the impact of time and patient characteristics on hospitalizations is important for resource allocation planning and designing future research studies examining interventions and treatments for MS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 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".