Severe Acute Respiratory Syndrome Coronavirus 2 and Coronavirus Disease 2019: A Clinical Overview and Primer
Bibliographic record
Abstract
Following its emergence in December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused what rapidly became a global pandemic. The precise origin and subsequent path of transmission have not yet been established-but like the other novel coronaviruses that it closely resembles, it appears to have evolved naturally in a bat host. The disease caused by SARS-CoV-2 infection, designated as coronavirus disease 2019 (COVID-19), ranges from asymptomatic, to mild self-limited illness, to progressive pneumonia, respiratory compromise, multiorgan failure, and death. In addition, a hyperinflammatory disease state occurs in a subset of patients, and may be seen either during acute infection or following recovery. The search for effective pharmacological management of COVID-19 continues, but several promising candidates have been identified, including the viral nucleoside analog remdesivir. However, despite the existence of literally thousands of clinical trials, the management of COVID-19 remains challenging, and the development of an optimal, evidence-based therapeutic approach is ongoing. The impact of SARS-CoV-2 and COVID-19 on the biobanking world is evolving and profound-in particular, it is likely that many of mysteries surrounding COVID-19 will be solved via the availability of high-quality, large-scale collection, storage, and analysis of patient specimens. The purpose of this review article is therefore to provide a rapid, comprehensive, and relevant overview and primer on SARS-CoV-2 and COVID-19, with attention to the epidemiology, virology, transmission, clinical features, and major therapeutic options currently existent.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".