Current Drugs with Potential for Treatment of COVID-19: A Literature Review
Bibliographic record
Abstract
PURPOSE: SARS-CoV-2 first emerged in China in December 2019 and rapidly spread worldwide. No vaccine or approved drug is available to eradicate the virus, however, some drugs that are indicated for other afflictions seems to be potentially beneficial to treat the infection albeit without unequivocal evidence. The aim of this article is to review the published background on the effectiveness of these drugs against COVID-19 Methods: A thorough literature search was conducted on recently published studies which have published between January 1 to March 25, 2020. PubMed, Google Scholar and Science Direct databases were searched Results: A total 22 articles were found eligible. 8 discuss about treatment outcomes from their applied drugs during treatment of COVID-19 patients, 4 report laboratory tests, one report animal trial and other 9 articles discuss recommendations and suggestions based on the treatment process and clinical outcomes of other diseases such as malaria, ebola, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). The data and/or recommendations are categorized in 4 classes: (a) anti-viral and anti-inflammatory drugs, (b) anti-malaria drugs, (c) traditional Chinese drugs and (d) other treatments/drugs. CONCLUSION: All examined treatments, although potentiality effective against COVID-19, need either appropriate drug development or clinical trial to be suitable for clinical use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".