Abstract P82: The Impact of SARS-COV-2 on Stroke Epidemiology and Care: A Meta-Analysis
Bibliographic record
Abstract
Background: Emerging data indicates an increased risk for cerebrovascular events with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and highlights the potential impact of coronavirus disease (COVID-19) on the management and outcomes of acute stroke. We conducted a systematic review and meta-analysis to evaluate the aforementioned considerations. Methods: We performed a meta-analysis of observational cohort studies reporting on the occurrence and/or outcomes of patients with cerebrovascular events in association with their SARS-CoV-2 infection status. We used a random-effects model. Summary estimates were reported as odds ratios (ORs) and corresponding 95% confidence intervals (95%CI). Results: We identified 16 cohort studies including 44,004 patients. Among patients with SARS-CoV-2, 1.3% (95%CI: 0.9-1.8%; I 2 =88%) were hospitalized for cerebrovascular events, 1.2% (95%CI: 0.8-1.5%; I 2 =85%) for ischemic stroke, and 0.2% (95%CI: 0.1-0.4%; I 2 =69%) for hemorrhagic stroke. Compared to non-infected contemporary or historical controls, patients with SARS-CoV-2 infection had increased odds of ischemic stroke (OR=3.58, 95%CI: 1.43-8.92; I 2 =43%) and cryptogenic stroke (OR=3.98, 95%CI: 1.62-9.77; I 2 =0%). Odds for in-hospital mortality were higher among SARS-CoV-2 stroke patients compared to non-infected contemporary or historical stroke patients (OR=5.60, 95%CI: 3.19-9.80; I 2 =45%). SARS-CoV-2 infection status was not associated to the likelihood of receiving intravenous thrombolysis (OR=1.42, 95%CI: 0.65-3.10; I 2 =0%) or endovascular thrombectomy (OR=0.78, 95%CI: 0.35-1.74; I 2 =0%) among hospitalized ischemic stroke patients during the COVID-19 pandemic. Diabetes mellitus was found to be more prevalent among SARS-CoV-2 stroke patients compared to non-infected contemporary or historical controls (OR=1.39, 95%CI: 1.04-1.86; I 2 =0%). Conclusion: SARS-CoV-2 appears to be associated with an increased risk of ischemic stroke, particularly the cryptogenic subtype. SARS-CoV-2 infection in stroke substantially increases the mortality risk.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.071 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".