Worldwide 1-month case fatality of ischaemic stroke and the temporal trend
Bibliographic record
Abstract
BACKGROUND: The 1-month case fatality of ischaemic stroke is an essential epidemiological metric. Whereas the case fatality after ischaemic stroke and the temporal trend is uncertain. We aimed to estimate the 1-month case fatality of ischaemic stroke and its temporal trend, as well as its regional variation. METHODS: We searched PubMed and Embase to identify the studies for 1-month case fatality of ischaemic stroke . The population-based studies were included. Two investigators extracted the data and assessed the quality independently. One-month case fatality of ischaemic stroke was estimated using a random effects model. The temporal trend was evaluated using a mixed-effect meta-regression model. RESULTS: A total of 59 articles with 77 time periods were included. The worldwide 1-month case fatality of ischaemic stroke was 13.5% (95% CI 12.3% to 14.7%). The case fatality was 10.8% (95% CI 8.3% to 13.5%) in Asia, 14.2% (95% CI 12.6% to 15.9%) in Europe, 14.0% (95% CI 11.2% to 17.1%) in South America and Caribbean, 14.0% (95% CI 9.5% to 19.1%) in North America and 12.5% (95% CI 11.1% to 13.9%) in Australia and New Zealand. Overall, there was a non-significant decrease of 0.1% per year in case fatality. It decreased significantly in Europe (-0.2% annually, 95% CI -0.4% to -0.01%) and North America (-0.2% annually, 95% CI -0.4% to -0.04%), increased significantly in Australia and New Zealand (0.2% annually, 95% CI 0.1% to 0.4%), while no evidence of change in other regions. CONCLUSION: The 1-month case fatality of ischaemic stroke and its temporal trend were divergent across regions. Further studies are needed to address the reason of the regional difference, which will be helpful to guide the effort of reducing stroke burden.
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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 | 0.039 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.016 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".