Anticoagulation in Patients With COVID-19
Bibliographic record
Abstract
Clinical, laboratory, and autopsy findings support an association between coronavirus disease-2019 (COVID-19) and thromboembolic disease. Acute COVID-19 infection is characterized by mononuclear cell reactivity and pan-endothelialitis, contributing to a high incidence of thrombosis in large and small blood vessels, both arterial and venous. Observational studies and randomized trials have investigated whether full-dose anticoagulation may improve outcomes compared with prophylactic dose heparin. Although no benefit for therapeutic heparin has been found in patients who are critically ill hospitalized with COVID-19, some studies support a possible role for therapeutic anticoagulation in patients not yet requiring intensive care unit support. We summarize the pathology, rationale, and current evidence for use of anticoagulation in patients with COVID-19 and describe the main design elements of the ongoing FREEDOM COVID-19 Anticoagulation trial, in which 3,600 hospitalized patients with COVID-19 not requiring intensive care unit level of care are being randomized to prophylactic-dose enoxaparin vs therapeutic-dose enoxaparin vs therapeutic-dose apixaban. (FREEDOM COVID-19 Anticoagulation Strategy [FREEDOM COVID]; NCT04512079).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".