Abstract 14699: Arterial and Venous Thromboembolism in Covid-19: A Study-level Meta-analysis
Bibliographic record
Abstract
Introduction: The prevalence of venous (VTE) and arterial (ATE) thromboembolic events in patients with COVID-19 remains largely unknown. Methods: In this systematic review and meta-analysis, we systematically searched Pubmed MEDLINE, Google Scholar, and Web of Science for observational studies describing the prevalence of VTE and ATE amongst patients with COVID-19 published between Jan 1, 2020 and May 20, 2020. The websites of major journals were also searched. Results: We analysed findings from 15 studies totalizing 1,755 patients, mainly in intensive care units (ICU). The weighted frequency of COVID-19-related VTE was 16.6% (95%CI 9.5-25.1%, I 2 =94%, 13 studies: 1,546 patients). The overall prevalence of PE and deep vein thrombosis (DVT) were 9.1% (95%CI 3.7-16.6%, I 2 =95%; 15 studies; 1,755 patients) and, 7.8% (95%CI 3.4-13.8%; I 2 =92%; 13 studies; 1,445 patients), respectively. Few were isolated subsegmental PE or distal DVT. The VTE prevalence was significantly higher in ICU (21.6%; 95%CI 12.6-32.2%; I2=91% versus 4.6%; 95%CI 1.0-10.7%, I 2 =87%, p interaction =0.002 in subgroup analysis). The weighted frequency of myocardial infarction/acute coronary syndrome, stroke, and other ATE (6 studies, 812 patients) was 3.2% (95%CI 2.1-4.5%, I 2 =0%), 0.7% (95%CI 0.0-2.2%, I 2 =64%), 2.0% (95%CI 1.2-3.0%, I 2 =40%), and 0.5% (95%CI 0.0-1.6%, I 2 =60%), respectively. Conclusions: Patients admitted in the ICU for severe COVID-19 had a high risk of VTE. Conversely, further studies are needed to determine the specific effects of COVID-19 on the risk of ATE or VTE in less severe forms of the disease.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.053 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".