A compendium answering 150 questions on COVID‐19 and SARS‐CoV‐2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 ~9 million confirmed cases and caused almost 500 000 related deaths worldwide. Unequivocally, the COVID-19 pandemic is the gravest health and socioeconomic 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, thromboembolic 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. A total of 150 questions were answered by experts in the field providing a comprehensive and practical overview of COVID-19 and allergic disease.
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.
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 | 0.022 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it