A new infectious disease challenge: Urbani severe acute respiratory syndrome (SARS) associated coronavirus
Bibliographic record
Abstract
Most community acquired pneumonias are bacterial and the hospitalised patient, often elderly, is rapidly rendered pathogen free by broad spectrum antibiotics.There is no particular threat of pathogen transmission to other patients or to hospital nurses or doctors.It was therefore an unpleasant and unexpected surprise when a cluster of hospital staff and trainee medical students in a Hong Kong hospital became ill with cough, breathlessness and a high temperature.These were all contacts with a 64-year-old doctor who had been admitted into the hospital with the initial diagnosis of community acquired pneumonia.This was the first indication in Hong Kong that a viral not bacterial infection, having apparently arisen in the nearby and adjacent province of Guangdong several months earlier, had spread to the colony. 1 A WHO epidemiologist, C. Urbani, categorised the new clinical syndrome severe acute respiratory syndrome (SARS) in Vietnam in February and later died of the virus which is now named after him.As politicians are apt to tell us, the rest is history.Whilst acknowledging that the scientific and medical communities will be in the lower range of a long ladder of step by step learning, it is already clear that modern molecular virology can identify a new virus and devise molecular testing with speed.However, on the negative side there is international and national panic.It is important that the scientific community appreciates how a modern society in the 21st century reacts to an infectious disease threat.This information will be of value to the WHO, which has issued a template plan for preparation in the event of a global outbreak of a much more contagious and life threatening disease, namely emergent influenza A virus.Why do new respiratory viruses arise in south-east Asia?
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".