European Multicenter Study of ET-COVID-19
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Acute ischemic stroke and large vessel occlusion can be concurrent with the coronavirus disease 2019 (COVID-19) infection. Outcomes after mechanical thrombectomy (MT) for large vessel occlusion in patients with COVID-19 are substantially unknown. Our aim was to study early outcomes after MT in patients with COVID-19. METHODS: Multicenter, European, cohort study involving 34 stroke centers in France, Italy, Spain, and Belgium. Data were collected between March 1, 2020 and May 5, 2020. Consecutive laboratory-confirmed COVID-19 cases with large vessel occlusion, who were treated with MT, were included. Primary investigated outcome: 30-day mortality. SECONDARY OUTCOMES: early neurological improvement (National Institutes of Health Stroke Scale improvement ≥8 points or 24 hours National Institutes of Health Stroke Scale 0-1), successful reperfusion (modified Thrombolysis in Cerebral Infarction grade ≥2b), and symptomatic intracranial hemorrhage. RESULTS: We evaluated 93 patients with COVID-19 with large vessel occlusion who underwent MT (median age, 71 years [interquartile range, 59-79]; 63 men [67.7%]). Median pretreatment National Institutes of Health Stroke Scale and Alberta Stroke Program Early CT Score were 17 (interquartile range, 11-21) and 8 (interquartile range, 7-9), respectively. Anterior circulation acute ischemic stroke represented 93.5% of cases. The rate modified Thrombolysis in Cerebral Infarction 2b to 3 was 79.6% (74 patients [95% CI, 71.3-87.8]). Thirty-day mortality was 29% (27 patients [95% CI, 20-39.4]). Early neurological improvement was 19.5% (17 patients [95% CI, 11.8-29.5]), and symptomatic intracranial hemorrhage was 5.4% (5 patients [95% CI, 1.7-12.1]). Patients who died at 30 days exhibited significantly lower lymphocyte count, higher levels of aspartate, and LDH (lactate dehydrogenase). After adjustment for age, initial National Institutes of Health Stroke Scale, Alberta Stroke Program Early CT Score, and successful reperfusion, these biological markers remained associated with increased odds of 30-day mortality (adjusted odds ratio of 2.70 [95% CI, 1.21-5.98] per SD-log decrease in lymphocyte count, 2.66 [95% CI, 1.22-5.77] per SD-log increase in aspartate, and 4.30 [95% CI, 1.43-12.91] per SD-log increase in LDH). CONCLUSIONS: The 29% rate of 30-day mortality after MT among patients with COVID-19 is not negligible. Abnormalities of lymphocyte count, LDH and aspartate may depict a patient's profiles with poorer outcomes after MT. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT04406090.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".