Changes in Stroke Hospital Care During the COVID-19 Pandemic: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background and Purpose: We systematically evaluated the impact of the coronavirus 2019 (COVID-19) pandemic on stroke care across the world. Methods: Observational studies comparing characteristics, acute treatment delivery, or hospitalization outcomes between patients with stroke admitted during the COVID-19 pandemic and those admitted before the pandemic were identified by Medline, Scopus, and Embase databases search. Random-effects meta-analyses were conducted for all outcomes. Results: We identified 46 studies including 129 491 patients. Patients admitted with stroke during the COVID-19 pandemic were found to be younger (mean difference, −1.19 [95% CI, −2.05 to −0.32]; I 2 =70%) and more frequently male (odds ratio, 1.11 [95% CI, 1.01–1.22]; I 2 =54%) compared with patients admitted with stroke in the prepandemic era. Patients admitted with stroke during the COVID-19 pandemic, also, had higher baseline National Institutes of Health Stroke Scale scores (mean difference, 0.55 [95% CI, 0.12–0.98]; I 2 =90%), higher probability for large vessel occlusion presence (odds ratio, 1.63 [95% CI, 1.07–2.48]; I 2 =49%) and higher risk for in-hospital mortality (odds ratio, 1.26 [95% CI, 1.05–1.52]; I 2 =55%). Patients with acute ischemic stroke admitted during the COVID-19 pandemic had higher probability of receiving endovascular thrombectomy treatment (odds ratio, 1.24 [95% CI, 1.05–1.47]; I 2 =40%). No difference in the rates of intravenous thrombolysis administration or difference in time metrics regarding onset to treatment time for intravenous thrombolysis and onset to groin puncture time for endovascular thrombectomy were detected. Conclusions: The present systematic review and meta-analysis indicates an increased prevalence of younger patients, more severe strokes attributed to large vessel occlusion, and higher endovascular treatment rates during the COVID-19 pandemic. Patients admitted with stroke during the COVID-19 pandemic had higher in-hospital mortality. These findings need to be interpreted with caution in view of discrepant reports and heterogeneity being present across studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".