Geographical Disparities in Pooled Stroke Incidence and Case Fatality in Mainland China, Hong Kong, and Macao: Protocol for a Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Geographical variations in stroke incidence and case fatality in China have been reported. Nonetheless, pooled estimates in major Chinese regions are unknown. Objective: This systematic review and meta-analysis aims to investigate pooled estimates of incidence and short-term case fatality of stroke in Mainland China, Hong Kong, and Macao. Methods: Longitudinal studies published in English and indexed in PubMed/MEDLINE, Embase, CINAHL, and Web of Science, or in Chinese and indexed in SinoMed and CQVIP will be targeted. Articles reporting on adults living in China who experience first-ever stroke or die within 1 year from newly onset stroke will be included. The 95% confidence intervals of the event will be estimated using the exact method based on the Poisson distribution. The log incidence rates together with their corresponding log standard errors will be meta-analyzed using DerSimonian and Laird random effects models. Pooled case fatality rates will also be estimated using a random effect model. Time trends in pooled age-standardized stroke incidence and case fatality will be estimated. The heterogeneity of the included studies will be measured using the I2 statistic and meta-regressions will be run to analyze the effect of reported covariates on found heterogeneity. Risk of bias will be examined using the Newcastle-Ottawa Scale. Publication bias will be tested using funnel plots and Egger tests. Sensitivity analysis will be run by risk of bias. Results: This study was funded and registered in 2020. The systematic searches, study selections, and quality assessments were completed in July 2021. Data extraction and analysis and manuscript writing are scheduled to be completed by December 2021. Conclusions: This will be the first study to provide regional differences in pooled estimates of stroke incidence with case fatality in Mainland China, Hong Kong, and Macao. This study will assist in addressing inequalities in stroke care across China.
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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 | 0.061 | 0.086 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.005 |
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".