The COVID-19 Pandemic: Disproportionate Thrombotic Tendency and Management Recommendations
Bibliographic record
Abstract
COVID-19 is an infectious disease caused by the SARS COV-2 virus. Patients with COVID-19 are susceptible to thrombosis due to excessive inflammation, platelet activation, endothelial dysfunction, and circulatory stasis, resulting in an increased risk of death due to associated coagulopathies. In addition, many patients receiving antithrombotic therapy for pre-existing thrombotic diseases can develop COVID-19, which can further complicate dose adjustment, choice and laboratory monitoring of antithrombotic treatment. This review summarizes the laboratory findings, the prohemostatic state, incidence of thromboembolic events and some potential therapeutic interventions of COVID-19 associated coagulopathy. We explore the roles of biomarkers of thrombosis and inflammation according to the severity of COVID-19. While therapeutic anticoagulation has been used empirically in some patients with severe COVID-19 but without thrombosis, it may be preferable to provide supportive care based on evidence-based randomized clinical trials. The likely lifting of travel restrictions will accelerate the spread of COVID-19, increasing morbidity and mortality across nations. Many individuals will continue to receive anticoagulation therapy regardless of their location, requiring on-going treatment with low-molecular weight heparin, vitamin K antagonist or direct-acting anticoagulants.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".