Efficacy and Safety of Anticoagulants for COVID-19 Patients in the Intensive Care Unit: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: This study aims to analyze the efficacy and safety of anticoagulants for COVID-19 patients in the intensive care unit. METHODS: A comprehensive search was conducted using databases such as MEDLINE, PubMed, EuropePMC, Science Direct, Google Scholar, Clinicaltrial.gov, The Cochrane Central Register of Controlled Trial (CENTRAL, Cochrane Library) and several other published articles from the systematic review up to March 31, 2021. The Newcastle-Ottawa Scale (NOS) was used for the studies' qualitative assessment. The primary outcome examined was mortality rate, while the secondary included the length of stay (LOS) in thei care unit; hospital length of stay (HOS), coagulation markers including D-dimer, Platelet count, aPTT, PT and fibrinogen; markers of inflammation specifically C-reactive protein; and other adverse events ranging from hemorrhage to thrombosis. Additionally, the quantitative synthesis was conducted using fixed and random effects model in "The Revman 5.4", while heterogeneity was tested using the I-squared (I2) measure. RESULTS: A total of 1,062 articles were found during the initial search step and eventually 12 were chosen to be analyzed quantitatively in a meta-analysis. Comparison of the results related to anticoagulant group with no anticoagulant or standard care treatment showed that anticoagulant group significantly reduced mortality rate with RR= 0.53; 95 % CI, 0.30-0.95; P= 0.03, with I2 = 88% and venous thromboembolism (VTE) RR = 0.53; 95% CI, 0.37-0.76; P = .0007 with I2 = 35%. CONCLUSIONS: Based on the results, anticoagulants can mitigate mortality rate and VTE in COVID-19 patients.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.041 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".