Antithrombotic therapy in COVID-19 patients
Bibliographic record
Abstract
Recently, there have been published the data of large randomized trials on the use of antithrombotic agents for reducing a risk of thromboembolic complications, multiple organ failure and mortality in COVID-19 patients. However, principles of selecting optimal therapy remain open. Strategies for the use of antithrombotic drugs in outpatient and inpatient settings, thromboprophylaxis in specific patient populations, and treatment of acute thrombosis in hospitalized COVID-19 patients are being developed. In October 2021, the International Society on Thrombosis and Hemostasis (ISTH) formed an interdisciplinary international panel of experts to develop recommendations for use of anticoagulants and antiplatelet agents in COVID-19 patients. Expert opinions are published. Here, we summarize all the publications available globally at the present time on this issue, obtained by using the principles of evidence-based medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.247 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".