Hypercoagulopathy in COVID-19, Deep Venous Thrombosis After Extra-Corporeal Membrane Oxygenation Therapy: A Case Report
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that has caused a pandemic across the world in the years 2019 - 2020 with over 100 countries reporting high infection rates. The virus is unique in the wide spectrum of disease manifestations it can cause; one of the worst of which is the hypercoagulable state induced by severe infection. This case report focuses on a 33-year-old Hispanic male who developed severe acute respiratory syndrome requiring management with extracorporeal membrane oxygenation (ECMO) and developed deep venous thromboses during severe coronavirus disease 2019 (COVID-19) pneumonia. Since there are no current guideline(s) for routine screening for venous thromboembolism (VTE) in ECMO patients, we aim to highlight a proposed benefit of routine screening for VTE in patients with severe COVID-19 treated with ECMO pre-cannulation and post-decannulation, which minimizes the risk of cannulation-associated complications, as well as the risk of post-decannulation VTE respectively. While VTE is a known complication of ECMO therapy, the rates of increased incidence of VTE in patients with severe COVID-19 make the detection of such complications even more important to reduce overall morbidity and mortality.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| 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".