Large-Vessel Occlusion Stroke Associated with Covid-19: A Systematic Review and Meta-Analysis of Outcomes
Bibliographic record
Abstract
Background SARS-CoV-2 induced respiratory illness is increasingly being recognized to be associated with neurological manifestations including an increase in the incidence of strokes, particularly those induced by large vessel occlusion (LVO). Given this, the aim of present study was to determine the influence of SARS-CoV-2 i.e. Coronavirus disease-19 (COVID-19) on mortality, neurological outcomes, and treatment response in patients with stroke due to large vessel occlusion induced by COVID-19.Methods A search of randomized controlled trials (RCTs), prospective and retrospective cohort studies was conducted through PUBMED, SCOPUS, MEDLINE, EMBASE, the Central Cochrane Registry of Controlled Trials, and CINAHL databases. The statistical analysis was performed using the relative risk with the Mantel-Haenszel methodology for dichotomous variables with a fixed-effects model. The Newcastle-Ottawa scale (NOS) was used to assess the quality of the publications and ROBINS-I tool was used to evaluate the risk of bias across the studies.Results Six retrospective observational cohort and case-control studies involving 1000 patients with LVO were included. The group of COVID 19 patients with LVO had a greater risk of mortality(OR= 7.09, [95% CI: 4.6-10.91], I2= 0%, p = <0.00001), fewer rates of treatment success(OR 0.15 [95% CI 0.08-0.29], I2 = 49%, p = <0.00001), and lower favorable outcomes (OR 0.39 [95% CI 0.16-0.96], I2 = 63%, p = 0.04) than COVID 19 negative patients with LVO.Conclusion The findings from present systematic review suggest that patients with COVID 19 and LVO stroke have higher mortality and poorer outcomes than COVID 19 negative patients with LVO stroke.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".