Role of mechanical thrombectomy among large vessel stroke patients during the coronavirus disease (COVID-19) pandemic
Bibliographic record
Abstract
Abstract Purpose The aim of our study is to provide insights derived from experience at multiple centers regarding the outcomes of mechanical thrombectomy (MT) for large vessel occlusion (LVO) in COVID-19 patients and compare them with those in non-COVID-19 patients during the coronavirus disease (COVID-19) pandemic. Results COVID‐19 positive patients were younger than COVID‐19 negative patients (62.1 ± 2.69 versus 69.5 ± 2.2, P < 0.001). There was a significant difference between COVID-19 and non-COVID-19 groups in the median D-dimer levels (6 vs. 4.5; P < 0.001), median ESR levels (63 vs. 38; P < 0.001) and median CRP levels (110 vs. 48.5; P < 0.001), respectively. Median time from stroke symptoms onset to hospital admission was significantly higher among COVID-19 positive patients (366 vs. 155 min; P < 0.001). COVID‐19 positive patients with LVO presented with a higher median NIH Stroke Scale score at presentation (16 versus 8, P < 0.001) and lower median Alberta Stroke Program Early CT Score (ASPECTS) on admission (6 versus 8, P < 0.001). Patients with COVID-19 had significantly higher percentages of poor functional outcomes as scored using the mRS grades 3–5 in comparison to non-COVID-19 patients (69.2% vs. 13.6%; P = 0.002), but there was no significant difference between both groups in complications such as early cerebral re-occlusion, intracerebral hemorrhage, or in-hospital mortality (P > 0.05). Conclusion Mechanical thrombectomy has effectively managed patients with LVO stroke. LVO stroke in COVID-19 patients occur at a young age, and have multi-territory vascular involvement. Poor functional outcomes post thrombectomy in COVID-19 patients, irrespective of timely, successful angiographic recanalization.
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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".