Changes in the in-hospital mortality due to stroke and factors affecting its reduction in the European Union, Middle East, USA, Canada, Ethiopia and China
Bibliographic record
Abstract
Introduction. While a specialized healthcare system is being developed for patients with stroke, early in-hospital mortality has been rightly chosen as a target indicator. It integrally reflects the correctness of organizational decisions, the completeness and quality of the diagnostic process, the availability of help, and factors relating to patient comorbidities. Aim. To gather information reflecting the level of the in-hospital mortality due to stroke and factors leading to its reduction. Materials and methods. PubMed was used to search the Medline database with the keywords ‘mortality rate’, ‘in-hospital mortality’, ‘stroke’, and ‘prediction’. The review included sources in any language from the year 2000 to the present if the full text was available online. Most of the statistical data were obtained from national stroke registries. Results. Direct indicators of the percentage of in-hospital mortality varied significantly between different countries, precluding direct comparison. In-hospital mortality varied significantly and depended on clinical features and healthcare administration, including hospitals’ size and their level. A change in the in-hospital mortality was reported in 9 out of 22 reports and enabled us to track the degree of its reduction. The mean rate of reduction was 0.36% per year. Faster changes in this parameter were typical for ischaemic stroke and accompanied the implementation and expansion of cerebrovascular surgery centers with dedicated stroke units. We identified ‘modifiable’ and ‘non-modifiable’ factors that influence in-hospital mortality in stroke patients. Conclusion. To more accurately evaluate the role of factors affecting in-hospital mortality in different countries, a meta-analysis is required, which would consider the regional organizational features, the availability of trained specialists at cerebrovascular surgery centers, and the degree of population awareness. The most consistent predictors of the in-hospital mortality were age, stroke type, stroke location, level of consciousness according to the Glasgow Coma Scale, stroke severity as measured by the NIHSS, and comorbidities. Factors that can reduce in-hospital mortality rates include population awareness, increased availability of ICU beds for stroke patients, telehealth, monitoring for late complications, and primary prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".