Predictors of hospitalization in a Canadian MS population: A matched cohort study
Bibliographic record
Abstract
OBJECTIVE: Hospitalizations are the most costly component of healthcare in Canada, and hospitalization rates are higher in the multiple sclerosis (MS) population compared to the general population. This study aimed to examine predictors of hospitalizations in the MS population in Saskatchewan, Canada. METHODS: This retrospective cohort study used population-based health administrative data from Saskatchewan, Canada from 1996 to 2016. Subjects with MS were identified using a validated definition (≥3 hospital, physician, or drug claims for MS). Up to five general population controls were identified for each MS case and matched on sex, age, and geographical location. The rate of hospitalizations and reason for admission were determined for each case and control. Negative binomial (hospitalization rate) and binary logistic (reason for admission) regression models fitted with generalized estimating equations were used to test the following potential predictors: sex, age, median household income, calendar year, prior hospitalizations, and comorbidity status. RESULTS: We identified 4,878 MS cases (11,744 hospitalizations), and 23,662 matched controls (32,541 hospitalizations). Higher comorbidity burden, older age, and prior hospital admissions were associated with an increased rate of all-cause hospitalizations for both cohorts. Males were more likely to be hospitalized than females for all-cause (adjusted rate ratio: 1.20; 95% CI: 1.07 - 1.34) and MS-specific (adjusted odds ratio: 1.34; 95% CI 1.15 - 1.55) hospitalizations. The rate of MS-specific hospitalizations decreased with age, and there was no association with comorbidity or prior hospitalizations. A diagnosis of MS was associated with decreased odds of hospitalization due to neoplasms, diseases of the circulatory system, and mental health and behavioural disorders. CONCLUSION: Increased age, comorbidity, and prior hospital admissions are predictors of all-cause hospitalizations. Conversely, MS-related hospitalizations decreased as subjects aged, and there was no association with comorbidity. Our results highlight that reasons for hospitalizations can differ by age, and clinicians should consider this when managing patients, as they make efforts to reduce hospitalizations in the MS population.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".