The Society of Vascular and Interventional Neurology (SVIN) Mechanical Thrombectomy Registry: Outcomes in Patients With Acute Ischemic Stroke and COVID‐19
Bibliographic record
Abstract
Background: Clinical and radiographic outcomes after mechanical thrombectomy in the setting of COVID-19 infection remain poorly characterized. We sought to determine how COVID-19 status affects mechanical thrombectomy outcomes in the real-world setting in the United States. Methods: The prospectively maintained multicenter mechanical thrombectomy registry from the Society of Vascular and Interventional Neurology was queried for baseline clinical characteristics among patients with and without COVID-19 who underwent mechanical thrombectomy between March 1 and December 31, 2020 at 12 sites. Primary outcome was the likelihood of good neurological outcomes (90 day modified Rankin scale 0-2) among patients with COVID-19 treated with endovascular thrombectomy, which was assessed using multivariable logistic regression adjusted for age, National Institutes of Health Stroke Scale, Alberta Stroke Program Early CT Score, and substantial reperfusion (modified Thrombolysis in Cerebral Infarction 2b, 2c, and 3). Secondary outcomes included National Institutes of Health Stroke Scale at 24 hours. Results: Among 915 patients who underwent mechanical thrombectomy during the study period, 51 patients were positive for COVID-19 (5.6%). Univariate analysis revealed that compared with patients who were COVID-19 negative, patients who were positive for COVID-19 were more likely to be male, nonsmokers, have lower Alberta Stroke Program Early CT Score, and present with intracranial internal carotid artery occlusions (Table 1). They were also less likely to achieve successful reperfusion. Multivariable analysis, however, failed to identify any independent associations with COVID-19 positive status. Conclusion: In our cohort, patients postive for COVID-19 with acute ischemic stroke who undergo mechanical thrombectomy have similar baseline characteristics, imaging features, procedural, and clinical outcomes compared to patients who are negative for COVID-19 in multivariate analysis. Further analyses are warranted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".