The time course of subsequent hospitalizations and associated costs in survivors of an ischemic stroke in Canada
Bibliographic record
Abstract
BACKGROUND: Documentation of the hospitalizations rates following a stroke provides the inputs required for planning health services and to evaluate the economic efficiency of any new therapies. METHODS: Hospitalization rates by cause were examined using administrative data on 18,695 patients diagnosed with ischemic stroke (first or subsequent, excluding transient ischemic attack) in Saskatchewan, Canada between 1990 and 1995. Medical history was available retrospectively to January 1980 and follow-up was complete to March 2000. Analyses evaluated the rate and timing of all-cause and cardiovascular hospitalizations within discrete periods in the five years following the index stroke. Cardiovascular hospitalizations included patients with a primary diagnosis of ischemic stroke, transient ischemic attack, myocardial infarction, stable or unstable angina, heart failure or peripheral arterial disease. RESULTS: One-third (36%) of patients were identified by a hospitalized stroke. Mean age was 70.5 years, 48.0% were male, half had a history of stroke or a transient ischemic attack at the time of their index stroke. Three-quarters of the patients (72.7%) were hospitalized at least once during a mean follow-up of 4.6 years, accruing CAD $24 million in the first year alone. Of all hospitalizations, 20.4% were related to cardiovascular disease and 1.6% to bleeds. In the month following index stroke, 12.5% were admitted, an average of 1.04 times per patient hospitalized. Strokes accounted for 33% of all hospitalizations in the first month. The rate diminished steadily throughout the year and stabilized in the second year when approximately one-third of patients required hospitalization, at a rate of about one hospitalization for every two patient-years. Mean lengths of stay ranged from nine days to nearly 40 days. Close-fitting Weibull functions allow highly specific probability estimates. Other cardiovascular risk factors significantly increased hospitalization rates. CONCLUSION: After stroke, there are frequent hospitalizations accounting for substantial additional costs. Though these rates drop after one year, they remain high over time. The number of other cardiovascular causes of hospitalization confirms that stroke is a manifestation of disseminated atherothrombotic disease.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".