The influence of COVID-19 on short-term mortality in acute ischemic stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the differences in short-term mortality risk between acute ischemic stroke (AIS) patients with and without SARS-CoV-2 infection. METHODS: PubMed, EMBASE, Scopus, and Cochrane Databases were systematically searched from December 1, 2019 to May 20, 2022 using the keywords coronavirus disease 2019 (COVID-19), COVID-19, SARS-CoV-2, and ischemic stroke. A random-effects model was estimated, and subgroup analysis and meta-regressions were performed. The quality of eligible studies was assessed using the Newcastle-Ottawa Scale. RESULTS: A total of 26 eligible studies with 307,800 patients were included in this meta-analysis. The overall results show that in-hospital and 90-day mortality was 3.31-fold higher in AIS with SARS-CoV-2 patients compared with those without SARS-CoV-2. When matched for age and National Institutes of Health Stroke Scale score at admission, the risk ratio of in-hospital mortality from AIS among patients with SARS-CoV-2 versus without decreased to 2.83. Reperfusion therapy and endovascular thrombectomy may further reduce the risk of death in patients to some extent but do not increase the incidence of symptomatic intracerebral hemorrhage. Meta-regression showed that in-hospital mortality decreased with increasing National Institutes of Health Stroke Scale score in AIS with SARS-CoV-2 compared to those without SARS-CoV-2 and that the difference in mortality risk between the 2 was independent of age and sex. CONCLUSIONS: The results of this study suggest that AIS patients with SARS-CoV-2 have higher short-term mortality compared to AIS patients without SARS-CoV-2, and reperfusion and endovascular thrombectomy therapy may reduce the risk of short-term mortality to some extent. The differences in in-hospital mortality risk were similar across ages and sexes. Focused attention is therefore needed on AIS patients with SARS-CoV-2 to control mortality.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.042 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".