COVID-19: pathogenic mechanism, detection, and treatment
Bibliographic record
Abstract
COVID-19, which broke out in 2019 in China, is caused by SARS-CoV-2. According to the latest WHO real-time statistics, as of 1:19 p.m. BST on April 22, there were 142557268 cumulative new confirmed cases of coronavirus pneumonia and 3033798 cumulative deaths worldwide. Therefore, it is significant to understand the structure and the pathogenic mechanism of SARS-CoV-2, which is helpful to understand the diagnosis and treatment of sars-cov-2. We primarily investigate related sources on Pubmed and related scholar websites. We found some antiviral drugs such as lopinavir, chloroquine, and its derivative hydroxychloroquine, remdesivir, and arbidol have great potential in the treatment of COVID-19. However, some of them have a serious adverse reaction, reminding us to use them with caution.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".