Thromboembolic Complications in Covid-19: From Clinical Scenario to Laboratory Evidence
Bibliographic record
Abstract
SARS-Cov-2 infection, a pandemic disease since March 2020, is associated with a high percentage of cardiovascular complications mainly of a thromboembolic (TE) nature. Although clinical patterns have been described for the assessment of patients with increased risk, many TE complications occur in patients with apparently moderate risk. Notably, a recent statement from the European Society of Cardiology (ESC) atherosclerosis and vascular biology working group pointed out the key role of vascular endothelium for the recruitment of inflammatory and thrombotic pathways responsible for both disseminated intravascular coagulation and cardiovascular complications. Therefore, a better understanding of the pathophysiological process linking infection to increased TE risk is needed in order to understand the pathways of this dangerous liaison and possibly interrupt it with appropriate treatment. In this review, we describe the histological lesions and the related blood coagulation mechanisms involved in COVID-19, we define the laboratory parameters and clinical risk factors associated with TE events, and propose a prophylactic anticoagulation treatment in relation to the risk category. Finally, we highlight the concept that a solid risk assessment based on prospective multi-center data would be the challenge for a more precise risk stratification and more appropriate treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".