Bibliographic record
Abstract
Since the outbreak of the new coronavirus pneumonia (COVID-19) in December 2019, more than 23 million people worldwide have been diagnosed with SARS-CoV-2. In response to this pandemic, a global mobilization of scientific, industrial and political support has ensued. However, more than 8 months later, as studies multiply and several governments are embarking on a resumption of their activities, the threat still remains. Our efforts to understand the evolution of the virus and the means to defeat it, at the dawn of a possible new wave, have raised more questions than provided clear and unequivocal answers. Compared to diseases caused by previously known human coronavirus, COVID-19 shows higher transmissibility, as a matter of fact "deeply concerning" cases continue to increase. Under these circumstances, and based on the information we have collected so far, this paper provides an overview of the epidemiological status of COVID-19 by considering, first through comparisons with other coronaviruses, similarities that may guide prevention measures and potentially effective therapies. From this starting point, we aimed to discuss the evidence around the efficacy of masks and respirators for different group of the population. Finally, we address therapeutic aspects including perspectives of vaccines and some antimicrobial agents such as remdesivir, favipiravir, chloroquine, hydroxychloroquine in combination with azithromycin and immunomodulators.
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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 | 0.000 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".