Large Vessel Stroke Following Multiple Other Strokes and Cardiomyopathy in a Forty-Nine-Year-Old COVID-19 Patient
Bibliographic record
Abstract
The novel coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has made its presence known on the centerstage of worldwide healthcare in 2020. Although it is widely known about its pulmonary presence and ensuing complications, evidence is emerging that there are other organ systems including the cardiovascular and cerebrovascular systems that may be damaged by this virus. There have been reports of large vessel stroke occurring in coronavirus disease 2019 (COVID-19) positive patients, with very few reported in the age group less than 50 years. In this case, we describe a previously healthy 49-year-old male who presented with signs of stroke, and was found to have the novel coronavirus as he had been suffering from upper respiratory tract symptoms for 3 weeks. He subsequently developed further large vessel stroke while in the hospital despite being started on antiplatelet therapy. He was also found to have new onset cardiomyopathy. He was started on anticoagulation and discharged with follow-up for cardiomyopathy testing outpatient. This case begs the question on which anticoagulation to utilize in COVID-19 positive patients to be effective in preventing thrombotic events. It is postulated that a pro-inflammatory state induced by the virus and the virus' affinity for angiotensin converting enzyme-2 receptors in the cerebral vasculature are predispositions to cause a stroke. The virus also directly damages cardiac myocytes causing a number of cardiac complications including cardiomyopathy. It is crucial that guidelines on anticoagulation choice and indications for when to start anticoagulation be developed in order to prevent the more devastating consequences of thrombosis and embolism and their subsequent clinical sequelae.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".