Mortality Rate of Acute Stroke in Iran: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: There is limited data about the short-term stroke mortality rates for patients in treatment settings. Objectives: This study aimed to estimate the short-term stroke (in hospital, one month, one year) mortality rates in Iran through a systematic review and meta-analysis. Materials & Methods: We searched electronic databases, including three national (IranDoc, Megiran, SID) and four international (Scopus, PubMed, Web of Science, Google Scholar), from January 1990 to March 2020. We considered all observational studies on stroke mortality, such as cohort and crosssectional studies. Furthermore, the sub-group analyses were performed based on each province and metaregression analysis based on the study’s year and patients’ mean age. Results: Among 143 studies, 28 were eligible (11 cohort and 17 cross-sectional studies). Based on the random model, the mortality rates for in-hospital, 1-month, and 1-year mortality were reported as 18.71% (95% CI: 15.09%-22.34%), 23.43% (95% CI: 20.08%-26.78%), and 34.44% (95% CI: 32.02%- 36.85%), respectively. The results also revealed that mortality rates were neither related to the year studies conducted nor to the patient’s age. Conclusion: Approximately one-fifth of stroke patients in Iran die in the hospital after admission. The mortality rate increased in the one-month and one-year period, and about one-third of the patients died in the first year. Therefore, it is cardinal to focus on programs and solutions in which we can ameliorate mortality in the short-term period after stroke by performing primary specific treatments on patients.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".