A Meta-analysis of the Impact of COVID-19 on Stroke Mortality
Bibliographic record
Abstract
Background The COVID-19 pandemic seriously affects human health and life. COVID-19 has been reportedly associated with a high risk of thrombotic events, which are closely associated with stroke. Objective To assess the effect and possible mechanism of COVID-19 on stroke morbidity, providing a reliable theoretical basis for scientific prevention and treatment of COVID-19 in stroke. Methods We searched databases of Web of Science, PubMed, EmBase, Cochrane Library, CNKI and Wanfang Data for cohort studies and case-control studies related to COVID-19 and stroke published from December 2019 to January 2022. Two researchers conducted literature screening and data extraction separately. The Newcastle-Ottawa Scale was used to assess the quality of included studies. Meta-analysis was used to evaluate the impact of COVID-19 on stroke mortality. Funnel plot was used to evaluate the potential publication bias. Results A total of 18 studies were included, 12 of them were of good quality, and other 6 were of fair quality. Meta-analysis showed that stroke patients with COVID-19 had higher mortality〔RR=4.16, 95%CI (2.82, 6.13) , P<0.000 01〕, prolonged prothrombin time (PT) 〔MD=0.78, 95%CI (0.35, 1.20) , P=0.000 3〕, higher D-dimer level〔MD=1.34, 95%CI (0.83, 1.84) , P<0.000 01〕 and higher NIHSS score〔MD=6.66, 95%CI (4.54, 8.79) , P<0.000 01〕, as well as younger age〔MD=-2.04, 95%CI (-3.48, -0.61) , P=0.005〕than those without COVID-19. There was no statistically significant difference in activated partial thromboplastin time between stroke patients with and without COVID-19〔MD=2.51, 95%CI (-2.69, 7.71) , P=0.34〕. Funnel plot assessing potential publication bias in the impact of COVID-19 on stroke mortality was basically symmetrical. Conclusion COVID-19 could increase the risk of stroke mortality, which may be related to alterations in the coagulation system manifested by abnormal PT and D-dimer level and so on. And the outcomes of stroke patients with COVID-19 were associated with age and NIHSS score at admission.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.078 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".