Outcome Predictors of Stroke Mortality in the Neurocritical Care Unit
Bibliographic record
Abstract
Background: Risk factors for medium to long-term mortality after stroke are well-established but predictors of in-hospital stroke mortality are less clearly characterized. Kazakhstan has the highest age-standardized mortality rate from ischemic stroke in the world. Methods: We performed a retrospective analysis of patients with stroke who were admitted over a 3.5-years period to the neurocritical care unit of a tertiary care hospital in Nur-Sultan, Kazakhstan. Results: In total, 148 critically ill patients were included in the analysis (84 ischemic stroke, 64 hemorrhagic stroke). The mean age was 63 years, 45% were male and the mean Glasgow Coma Score (±SD) at baseline was 10.3 (±3.4). The in-hospital mortality rate was similar in patients with ischemic (36%) and hemorrhagic (39%) stroke (HR 0.88, 95%CI 0.48–1.60). Median survival was 38 days (range: 1–89 days) in patients with ischemic stroke and 39 days (range: 1–63 days) in patients with hemorrhagic stroke. Univariable analysis found that patients who had a lower Glasgow Coma Scale, were in coma and who had cerebral edema were more likely to die in-hospital (P = 0.04, 0.02, <0.01, respectively). Conclusions: Our analysis showed that mortality risk in critically ill patients with hemorrhagic stroke was closer to mortality risk in patients with ischemic stroke than has been reported in other analyses. Hypertension, chronic heart failure, ischemic heart disease and atrial fibrillation were the most frequent comorbidities in patients who developed severe (life-threatening) stroke. Coma and cerebral edema on admission appear to be associated with poor outcome. This is the first publication of in-hospital stroke mortality from a Central Asian population and could form the basis for future research including development of risk scores and identifying modifiable risk factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".