RESEARCH Border Screening for SARS
Bibliographic record
Abstract
With the rapid international spread of severe acute respiratory syndrome (SARS) from March through May 2003, Canada introduced various measures to screen airplane passengers at selected airports for symptoms and signs of SARS. The World Health Organization requested that all affected areas screen departing passengers for SARS symptoms. In spite of intensive screening, no SARS cases were detected. SARS has an extremely low prevalence, and the positive predictive value of screening is essentially zero. Canadian screening results raise questions about the effectiveness of available screening measures for SARS at international borders. The first cases of severe acute respiratory syndrome (SARS) in Canada were recognized almost simultaneously in Vancouver and Toronto. In Toronto, the index case was diagnosed on March 13, 2003, when a cluster of SARS cases was identified and traced back to a traveler from Hong Kong, who arrived in Canada on February 23, 2003 (1). Two epidemic waves of SARS occurred in Toronto (2), which resulted in a national total of 251 probable cases with 43 deaths. In the period that followed the initial reports of this new syndrome from Hong Kong and Vietnam, the disease spread rapidly to other countries by international airline travelers. On March 12, 2003, the World Health Organization (WHO) issued a global health alert (3) in response to the clusters of SARS in the Hong Kong Special
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".