Disease, Drugs and Dilemma: A Review of Cardiovascular Implications of Novel COVID-19
Bibliographic record
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) is one of the greatest threat and challenge being faced by the entire nations in the current era. Though it primarily affects the respiratory system, like other viral infections, cardiovascular complications such as myocarditis, acute coronary syndrome, exacerbation of heart failure, and arrhythmia are not uncommon in COVID-19. They were reported to be associated with poor outcome. In addition, emerging reports also showed that patients with pre-existing cardiovascular comorbidities are more prone to develop severe form of COVID-19. The factors found to be independently associated with an increased risk of death were the age older than 65 years, coronary artery disease, heart failure, cardiac arrhythmia, chronic obstructive pulmonary disease, and current smoking. Concern has been raised regarding a potential harmful effect of drugs like angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), hydroxychloroquine and azithromycin. Therefore, in this article, we will concisely explore the potential cardiovascular implications of COVID-19 with the help of existing literature. Clin Infect Immun. 2020;5(2):25-30 doi: https://doi.org/10.14740/cii109
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".