Management of patients on antithrombotic therapy with severe infections: a joint clinical consensus statement of the ESC Working Group on Thrombosis, the ESC Working Group on Atherosclerosis and Vascular Biology, and the International Society on Thrombosis and Haemostasis
Bibliographic record
Abstract
Patients with severe infections and a pre-existing indication for antithrombotic therapy, i.e. antiplatelet agents, anticoagulant drugs, or their combinations, require integrated clinical counselling among coagulation, infectious disease, and cardiology specialists, due to sepsis-induced coagulopathy that frequently occurs. Bacterial and viral pathogens constitute an increasing threat to global public health, especially for patients with ongoing antithrombotic treatment who have a high risk of thrombotic recurrences and high susceptibility to severe infections with increased morbidity and mortality. Similarly, sepsis survivors are at increased risk for major vascular events. Coagulopathy, which often complicates severe infections, is associated with a high mortality and obligates clinicians to adjust antithrombotic drug type and dosing to avoid bleeding while preventing thrombotic complications. This clinical consensus statement reviews the best available evidence to provide expert opinion and statements on the management of patients hospitalized for severe bacterial or viral infections with a pre-existing indication for antithrombotic therapy (single or combined), in whom sepsis-induced coagulopathy is often observed. Balancing the risk of thrombosis and bleeding in these patients and preventing infections with vaccines, if available, are crucial to prevent events or improve outcomes and prognosis.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".