Characteristics of a COVID-19 Cohort With Large Vessel Occlusion: A Multicenter International Study
Bibliographic record
Abstract
BACKGROUND: The mechanisms and outcomes in coronavirus disease (COVID-19)-associated stroke are unique from those of non-COVID-19 stroke. OBJECTIVE: To describe the efficacy and outcomes of acute revascularization of large vessel occlusion (LVO) in the setting of COVID-19 in an international cohort. METHODS: We conducted an international multicenter retrospective study of consecutively admitted patients with COVID-19 with concomitant acute LVO across 50 comprehensive stroke centers. Our control group constituted historical controls of patients presenting with LVO and receiving a mechanical thrombectomy between January 2018 and December 2020. RESULTS: The total cohort was 575 patients with acute LVO; 194 patients had COVID-19 while 381 patients did not. Patients in the COVID-19 group were younger (62.5 vs 71.2; P < .001) and lacked vascular risk factors (49, 25.3% vs 54, 14.2%; P = .001). Modified thrombolysis in cerebral infarction 3 revascularization was less common in the COVID-19 group (74, 39.2% vs 252, 67.2%; P < .001). Poor functional outcome at discharge (defined as modified Ranklin Scale 3-6) was more common in the COVID-19 group (150, 79.8% vs 132, 66.7%; P = .004). COVID-19 was independently associated with a lower likelihood of achieving modified thrombolysis in cerebral infarction 3 (odds ratio [OR]: 0.4, 95% CI: 0.2-0.7; P < .001) and unfavorable outcomes (OR: 2.5, 95% CI: 1.4-4.5; P = .002). CONCLUSION: COVID-19 was an independent predictor of incomplete revascularization and poor outcomes in patients with stroke due to LVO. Patients with COVID-19 with LVO were younger, had fewer cerebrovascular risk factors, and suffered from higher morbidity/mortality rates.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".