Coronavirus Disease 2019 and Hypertension: How Anti-hypertensive Drugs Affect COVID-19 Medications and Vice Versa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: As a medical problem, hypertension is one of the most common disorders in cardiovascular disease. High blood pressure has been identified as one of the most familiar risk factors for the ongoing COVID-19 pandemic. We planned to explore the possible interactions between anti-hypertensive agents and drugs targeting SARS-CoV-2 with broad investigations of these medications' mechanism of action and adverse effects. METHODS: Two co-authors searched the electronic databases (PubMed, Scopus, and Google Scholar) to collect papers relevant to the subject. The keywords searched were angiotensin-converting enzyme inhibitors (ACEI), angiotensin-II receptor blockers (ARBs), sympatholytic drugs (alpha-1 blockers, beta-blockers), vasodilators (calcium channel blockers, nitrates, and hydralazine), diuretics, chloroquine, hydroxychloroquine, lopinavir/ritonavir, remdesivir, favipiravir, interferons, azithromycin, anti-cytokine agents, glucocorticoids, anticoagulant agents, nitric oxide, and epoprostenol. RESULTS: QT prolongation, arrhythmia, hypokalemia, hypertriglyceridemia are the most dangerous adverse effects in the patients on COVID-19 medications and anti-hypertensive drugs. CONCLUSION: This review emphasized the importance of the potential interaction between drugs used against COVID-19 and anti-hypertensive agents. Therefore, caution must be exercised when these medications are being used simultaneously.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it