Effect of Anticoagulants or Antiplatelets Administration on Mortality Case in COVID-19 Patients with Acute Ischemic Stroke: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Acute ischemic stroke (AIS) is a life-threatening complication of COVID-19. This study aims to compare anticoagulant or antiplatelet administration on mortality cases in patients with COVID-19 and AIS. To know the mortality rate in COVID-19 patients with AIS after anticoagulants or antiplatelets therapies. We searched PubMed, ScienceDirect, and Google Scholar for a retrospective cohort study of anticoagulant or antiplatelet effects on mortality cases in COVID-19 and AIS patients. The retrospective cohort was screened using our eligibility criteria, and quality was assessed using the Newcastle Ottawa Scale. Heterogeneity was assessed using the I2 test, and publication bias was evaluated using a funnel plot. All analyses were performed using Review Manager 5.4. Seven retrospective cohort studies involving 58 patients (38 of whom received anticoagulant therapy) met the inclusion criteria. Our combined analysis showed that anticoagulation versus antiplatelet therapy in COVID-19 patients with AIS on the forest plot chart did not significantly affect mortality (OR: 0.9 95% CI 0.42-1.91 I2=0 %). The study showed no significant difference in the incidence of death between anticoagulants or antiplatelet agents to COVID-19 patients with AIS.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".