A compendium answering over 140 questions on COVID-19 and SARS-CoV-2
Bibliographic record
Abstract
In December 2019, China reported the first cases of the coronavirus disease 2019 (COVID-19). This disease, caused by the severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2), has developed into a pandemic. To date it has resulted in ~5.6 million confirmed cases and caused 353,334 related deaths worldwide. Unequivocally, the COVID-19 pandemic is the gravest health and socio-economic crisis of our time. In this context, numerous questions have emerged in demand of basic scientific information and evidence-based medical advice on SARS-CoV-2 and COVID-19. Although the majority of the patients show a very mild, self-limiting viral respiratory disease, many clinical manifestations in severe patients are unique to COVID-19, such as severe lymphopenia and eosinopenia, extensive pneumonia, a “cytokine storm” leading to acute respiratory distress syndrome, endothelitis, thrombo-embolic complications and multiorgan failure. The epidemiologic features of COVID-19 are distinctive and have changed throughout the pandemic. Vaccine and drug development studies and clinical trials are rapidly growing at an unprecedented speed. However, basic and clinical research on COVID-19-related topics should be based on more coordinated high-quality studies. This paper answers pressing questions, formulated by young clinicians and scientists, on SARS-CoV-2, COVID-19 and allergy, focusing on the following topics: virology, immunology, diagnosis, management of patients with allergic disease and asthma, treatment, clinical trials, drug discovery, vaccine development and epidemiology. Over 140 questions were answered by experts in the field providing a comprehensive and practical overview of COVID-19 and allergic disease.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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".