Trends in Stroke Presentations before and during the COVID-19 Pandemic: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There are reports of decline in the rates of acute emergency presentations during coronavirus disease 2019 (COVID-19) pandemic including stroke. We performed a meta-analysis of the impact of COVID-19 pandemic on rates of stroke presentations and on rates of reperfusion therapy. METHODS: Following the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines, we systematically searched the literature for studies reporting changes in stroke presentations and treatment rates before and during the COVID-19 pandemic. Aggregated data were pooled using meta-analysis with random-effect models. RESULTS: We identified 37 observational studies (n=375,657). Pooled analysis showed decline in rates of all strokes (26.0%; 95% confidence interval [CI], 22.4 to 29.7) and its subtypes; ischemic (25.3%; 95% CI, 21.0 to 30.0), hemorrhagic (27.6%; 95% CI, 20.4 to 35.5), transient ischemic attacks (41.9%; 95% CI, 34.8 to 49.3), and stroke mimics (45.6%; 95% CI, 33.5 to 58.0) during months of pandemic compared with the pre-pandemic period. The decline was most evident for mild symptoms (40% mild vs. 25%-29% moderate/severe). Although rates of intravenous thrombolytic (IVT) and endovascular thrombectomy (EVT) decreased during pandemic, the likelihood of being treated with IVT and EVT did not differ between the two periods, both in primary and in comprehensive stroke centers (odds ratio [OR], 1.08; 95% CI, 0.94 to 1.24 and OR, 0.95; 95% CI, 0.83 to 1.09, respectively). CONCLUSIONS: Rates of all strokes types decreased significantly during pandemic. It is of paramount importance that general population should be educated to seek medical care immediately for stroke-like symptoms during COVID-19 pandemic. Whether delay in initiation of secondary prevention would affect eventual stroke outcomes in the long run needs further study.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".