Managing thrombosis and cardiovascular complications of COVID-19: answering the questions in COVID-19-associated coagulopathy
Bibliographic record
Abstract
INTRODUCTION: The first patients with Coronavirus disease 2019 (COVID-19) emerged at the end of 2019. This novel viral infection demonstrated unique features that include prothrombotic clinical presentations. However, one year after the first occurrence, there remain many unanswered questions. We tried to address some of the important queries in this review. AREAS COVERED: We raised the following critical questions. 'Why is COVID-19 so hypercoagulable?', 'Why are most coagulation test results relatively normal?', 'Why is COVID-19-associated coagulopathy more thrombotic than most other infectious diseases?', 'Why is arterial thrombus formed frequently?', 'Is anticoagulant therapy for COVID-19 effective?', and 'Are there racial disparities in thrombosis in COVID-19?' EXPERT OPINION: There are commonalities and differences in the pathogeneses and clinical features between COVID-19 and other infectious diseases. Correct understanding will help discussing appropriate anticoagulation prophylaxis or treatment for thromboembolism.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".