Pharmacological Agents Targeting Thromboinflammation in COVID-19: Review and Implications for Future Research
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19), currently a worldwide pandemic, is a viral illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The suspected contribution of thrombotic events to morbidity and mortality in COVID-19 patients has prompted a search for novel potential options for preventing COVID-19-associated thrombotic disease. In this article by the Global COVID-19 Thrombosis Collaborative Group, we describe novel dosing approaches for commonly used antithrombotic agents (especially heparin-based regimens) and the potential use of less widely used antithrombotic drugs in the absence of confirmed thrombosis. Although these therapies may have direct antithrombotic effects, other mechanisms of action, including anti-inflammatory or antiviral effects, have been postulated. Based on survey results from this group of authors, we suggest research priorities for specific agents and subgroups of patients with COVID-19. Further, we review other agents, including immunomodulators, that may have antithrombotic properties. It is our hope that the present document will encourage and stimulate future prospective studies and randomized trials to study the safety, efficacy, and optimal use of these agents for prevention or management of thrombosis in COVID-19.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".