Treatment and Outcomes of Patients With Ischemic Stroke During COVID-19
Bibliographic record
Abstract
Background and Purpose: The coronavirus disease 2019 (COVID-19) pandemic has created challenges in the delivery of acute stroke care. In this study, we analyze the characteristics, evaluation, treatment, and in-hospital outcomes of patients presenting with acute ischemic stroke (AIS) pre-COVID-19 and during COVID-19. Methods: Get With The Guidelines-Stroke is a national registry of adults with stroke in the United States. Using this registry, we identified patients with a diagnosis of AIS before (n=39 113; November 1, 2019–February 3, 2020) and after (n=41 971; February 4, 2020–June 29, 2020) the first reported case of COVID-19 in the registry. Characteristics, treatment patterns, quality metrics, and in-hospital outcomes were compared between the 2 groups. Results: Stroke presentations decreased by an average of 15.3% per week in the during COVID-19 time period when compared with similar months in 2019. Compared with patients with AIS in the pre-COVID-19 era, patients in the COVID-19 time period had similar rates of intravenous alteplase and endovascular therapy, and similar door to computed tomography, door to needle, and door to endovascular therapy times. In adjusted models, inpatient mortality was similar between those presenting with AIS pre-COVID-19 and during COVID-19 (4.8% versus 5.2%; odds ratio, 1.05 [95% CI, 0.97–1.13]). Conclusions: Among hospitals participating in Get With The Guidelines-Stroke, patients presenting with AIS during COVID-19 received, with few exceptions, similar quality care and experienced similar risk-adjusted outcomes when compared with patients with AIS presenting pre-COVID-19. These findings demonstrate that stroke care in the United States remains robust during the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".