A.5 Neurovascular complications of veno-venous extracorporeal membrane oxygenation in critically ill COVID-19 patients
Bibliographic record
Abstract
Background: Veno-venous extracorporeal membrane oxygenation (VV-ECMO) is an invasive intervention for patients with respiratory failure associated with COVID-19. This meta-analysis aims to determine the incidence of neurovascular complications in COVID-19 patients requiring VV-ECMO. Methods: Systematic literature search of MEDLINE, Embase, PsycINFO, and Cochrane databases was performed to identify studies that reported neurovascular complications of adult COVID-19 patients on VV-ECMO for respiratory failure. Case series and reports were excluded. Studies with 95% or more of its patients on VV-ECMO were pooled for meta-analysis. Results: Eighteen studies (n=1968) were included for meta-analyses. In COVID-19 patients requiring VV-ECMO, the incidences of intracranial hemorrhage and ischemic stroke were 11% [95% CI, 8–15%] and 2% [95% CI, 1–3%], respectively. Intraparenchymal and subarachnoid hemorrhages accounted for 73% and 8% of all intracranial hemorrhages, respectively. The risk ratio of mortality in COVID-19 patients with neurovascular complications on VV-ECMO compared to patients without neurovascular complications was 2.24 [95% CI, 1.46–3.46]. Conclusions: COVID-19 patients requiring VV-ECMO have a higher incidence of intracranial hemorrhage compared to historical data in non-COVID-19 patients (11% vs. 8%), while the incidence of ischemic stroke is similar (2%) in both cohorts. COVID-19 patients with neurovascular complications on VV-ECMO are at an increased risk of death.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.036 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".