COVID-19 and the cardiovascular system: insights into effects and treatments
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19), an acute and highly transmissible infectious disease, has reached a pandemic level since 11 March 2020 and continues to challenge the healthcare system worldwide. The pathogenesis of COVID-19 is a complex process involving mechanisms that suppress the host antiviral and innate immune response, while triggering marked activation of coagulation and hyperinflammation leading to cytokine storm in severe COVID-19. This review summarizes current evidence related to COVID-19-associated cardiovascular severe illness and mortality, which encompasses life-threatening clinical manifestations, including myocardial injury, fulminant myocarditis, cardiac arrhythmia, and ischemic stroke. The onset of hypercoagulable state is consistent with increased venous thromboembolism including deep vein thrombosis and pulmonary embolism. Thromboembolic manifestations include arterial thrombotic events such as stroke, myocardial infarction, and limb ischemia. Several treatment strategies have been investigated to mitigate COVID-19-associated cardiovascular clinical manifestations. The prevalence of thrombo-inflammatory syndrome and subsequent cardiovascular dysfunction prompted the implementation of antithrombotic therapy and strategies targeting major pro-inflammatory cytokines involved in COVID-19 cytokine storm. The development of new guidelines for effective treatment strategies requires concerted efforts to refine our understanding of the mechanisms underlying cardiovascular disease and large-scale clinical trials to reduce the burden of COVID-19 hospitalization and mortality.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".