Overview: The history and pediatric perspectives of severe acute respiratory syndromes: Novel or just like SARS
Bibliographic record
Abstract
Many respiratory viral infections such as influenza and measles result in severe acute respiratory symptoms and epidemics. In the spring of 2003, an epidemic of coronavirus pneumonia spread from Guangzhou to Hong Kong and subsequently to the rest of the world. The WHO coined the acronym SARS (severe acute respiratory syndrome) and subsequently the causative virus as SARS-CoV. In the summer of 2012, epidemic of pneumonia occurred again in Saudi Arabia which was subsequently found to be caused by another novel coronavirus. WHO coined the term MERS (Middle East respiratory syndrome) to denote the Middle East origin of the novel virus (MERS-CoV). In the winter of 2019, another outbreak of pneumonia occurred in Wuhan, China which rapidly spread globally. Yet another novel coronavirus was identified as the culprit and has been named SARS-CoV-2 due to its similarities with SARS-CoV, and the disease as coronavirus disease-2019. This overview aims to compare and contrast the similarities and differences of these three major episodes of coronavirus outbreak, and conclude that they are essentially the same viral respiratory syndromes caused by similar strains of coronavirus with different names. Coronaviruses have caused major epidemics and outbreaks worldwide in the last two decades. From an epidemiological perspective, they are remarkably similar in the mode of spread by droplets. Special focus is placed on the pediatric aspects, which carry less morbidity and mortality in all three entities.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".