Bibliographic record
Abstract
INTRODUCTION In February 2003, physicians at a hospital in Hanoi, Vietnam, sought advice from the local WHO office regarding a patient who had presented with an unusual influenza-like illness (1). Dr Carlo Urbani, an infectious disease specialist who responded to the request soon notified the WHO of an outbreak of severe respiratory disease. In the ensuing weeks, it became clear that similar outbreaks were occurring in several locations including Hong Kong, southern China, and Canada, and that patients in diverse locations had stayed at the same hotel in Hong Kong. The syndrome was called severe acute respiratory syndrome (SARS) and was characterized by fever, chills or rigors, headache, and nonspecific symptoms such as malaise andmyalgias, followed by cough and dyspnea (2,3). Respiratory tract disease progressed to acute respiratory distress syndrome requiring intensive care and mechanical ventilation in more than 20% of patients. Prolonged hospitalizations associated with complications were reported, and advanced age was an independent correlate of adverse clinical outcome and increased mortality. The outbreak was notable for spread in health care settings, affecting large numbers of health care workers, and for a rapid dissemination to distant parts of the world by infected travelers.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.217 | 0.143 |
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".