Trends in Hospital Admission for Stroke in Calgary
Bibliographic record
Abstract
BACKGROUND: Stroke incidence has fallen since 1950. Recent trends suggest that stroke incidence may be stabilizing or increasing. We investigated time trends in stroke occurrence and in-hospital morbidity and mortality in the Calgary Health Region. METHODS: All patients admitted to hospitals in the Calgary Health Region between 1994 and 2002 with a primary discharge diagnosis code (ICD-9 or ICD-10) of stroke were included. In-hospital strokes were also included. Stroke type, date of admission, age, gender, discharge disposition (died, discharged) and in-hospital complications (pneumonia, pulmonary embolism, deep venous thrombosis) were recorded. Poisson and simple linear regression was used to model time trends of occurrence by stroke type and age-group and to extrapolate future time trends. RESULTS: From 1994 to 2002, 11642 stroke events were observed. Of these, 9879 patients (84.8%) were discharged from hospital, 1763 (15.1%) died in hospital, and 591 (5.1%) developed in-hospital complications from pneumonia, pulmonary embolism or deep venous thrombosis. Both in-hospital mortality and complication rates were highest for hemorrhages. Over the period of study, the rate of stroke admission has remained stable. However, total numbers of stroke admission to hospital have faced a significant increase (p=0.012) due to the combination of increases in intracerebral hemorrhage (p=0.021) and ischemic stroke admissions (p=0.011). Sub-arachnoid hemorrhage rates have declined. In-hospital stroke mortality has experienced an overall decline due to a decrease in deaths from ischemic stroke, intracerebral hemorrhage and sub-arachnoid hemorrhage. CONCLUSIONS: Although age-adjusted stroke occurrence rates were stable from 1994 to 2002, this is associated with both a sharp increase in the absolute number of stroke admissions and decline in proportional in-hospital mortality. Further research is needed into changes in stroke severity over time to understand the causes of declining in-hospital stroke mortality rates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".