Ethical and Legal Challenges Posed by Severe Acute Respiratory Syndrome Implications for the Control of Severe Infectious Disease Threats
Bibliographic record
Abstract
Abstract Not long after the first reports of what ultimately would be called severe acute respiratory syndrome (SARS) began to appear in February 20031, 2 and as nations and the international community began to confront the spread of the new disease, it became clear that a host of ethical and legal issues had begun to surface. Indeed, not since the first years of the HIV/AIDS pandemic in the mid-l980s and the alarm over multidrug-resistant tuberculosis in the early 1990sdid it seem that so many issues touching on the core ethical questions posed by public health had to be addressed simultaneously. In several respects, SARS took society back to a pretherapeutic era with no definitive diagnostic test, a nonspecific case definition, and no effective vaccine or treatment. From I November 2002, to 1 July 2003, 8,445 cases were reported to the World Health Organization (WHO); among these, 5,327 (63%) were from China, 1,755 (20%) from Hong Kong, 678 (8%) from Taiwan, 252 (3%) from Canada, and 206 (2%) from Singapore. There were 812 deaths. Comparatively, the United States, with 73 cases (0.9%) and no deaths, was spared.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".