Variables Associated With 7-Day, 30-Day, and 1-Year Fatality After Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Seven-day, 30-day, and 1-year case-fatality indicators have been used to compare stroke care among hospitals, provinces, and countries and to implement quality improvement strategies. However, limited information is available concerning variables associated with stroke case fatality at these different points in time. We sought to identify and compare variables associated with 7-day, 30-day, and 1-year stroke fatality. METHODS: This was a cohort study of consecutive patients with acute ischemic stroke admitted to 11 stroke centers in Ontario, Canada, between July 2003 and March 2005 and captured in the Registry of the Canadian Stroke Network (RCSN). The RCSN database was linked to administrative databases to capture all deaths occurring within 7, 30, and 365 days of hospital admission for ischemic stroke. Logistic regression was used to determine variables associated with stroke fatality at each time point. Outcome measures were all-location mortality within 7 days, 30 days, and 1 year of hospital admission. RESULTS: Our cohort included 3631 patients admitted with ischemic stroke. Seven-day case fatality was 6.9% (249/3631), 30-day case fatality was 12.6% (457/3631), and 1-year case fatality was 23.6% (856/3631). In the multivariable analyses, stroke severity, neurologic deterioration during hospitalization, nonuse of antithrombotics during hospital admission, and lack of assessment by a stroke team were the most consistent predictors of case fatality at 7 days, 30 days, and 1 year after stroke. Physician experience in stroke management was inversely associated with 7-day and 30-day mortality, whereas age, comorbid illness, and pneumonia during hospital admission were associated with 30-day and 1-year mortality. CONCLUSIONS: Stroke severity and certain processes of care were associated with case fatality at 7 days, 30 days, and 1 year after stroke. This information may be useful for comparing risk-adjusted case-fatality rates among hospitals and for implementing strategies to improve the processes and quality of care in the acute phase of stroke.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".