Management of COVID-19 - A Review
Bibliographic record
Abstract
Abstract:: Coronavirus disease-2019(COVID-19) has gained much popularity not only in the Wuhan city of China but internationally also; in January 2020, the corona rapidly spread to many countries like the USA, Italy, Russia, India, Singapore, Pakistan, Thailand, Canada, Australia, England, and so on through passengers traveling to other countries. Corona patients can be cured with synthetic drugs, traditional herbal medicines (THM), use of Vitamin D and the quarantine approach. Different allopathic medicines, herbal extracts, and vitamin D have been observed to be useful in the treatment of novel coronavirus, like Remdesivir, hydroxychloroquine, Teicoplanin, Lopinavir+ Ritonavir, Ribavirin + corticosteroids, Glycyrrhizin, Sanguisorbae radix, Acanthopanacis cortex, Sophorae radix, etc. Various antiviral drugs are used to treat COVID-19, alone or in combination with other medications like Interferon-α, Lopinavir + Ritonavir, Arbidol, corticosteroids, etc., and some herbal extracts; also quarantine approach and Vitamin D are used that not only cure the infection but also boost up our immunity. For this review article, different papers were searched on Google Scholar, Scopus, WHO’s website, PubMed, clinicaltrials.gov and other relevant scientific research websites. In this review article, we have discussed the current strategies that are being used to treat COVID-19. Along with allopathic drugs, some herbal extracts can also be used to treat this novel coronavirus, like Glycyrrhizin, Sanguisorbae radix, Acanthopanacis cortex, Sophorae radix, etc. and even vitamin D.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".