acute respiratory syndrome: the Toronto experience
Bibliographic record
Abstract
In February 2003 the worldwide outbreak of severe acuterespiratory syndrome (SARS) reached Toronto. Thisfirst phase of the outbreak, lasting from March to May 2003, resulted in 257 probable and suspect cases and 27 deaths. Shortly after apparent containment of this outbreak, a second phase occurred, from May to June 2003, with an additional 119 probable and suspect cases and 17 deaths.1 Early in the outbreak, the search for the etiologic agent of SARS revealed a new coronavirus.2–4 Laboratory investi-gations in Canada added evidence that SARS cases in this country were associated with this new coronavirus.5 The rapid sequencing of the SARS-associated coronavirus (SARS-CoV) genome by 2 independent research groups enhanced the development of new molecular assays by vari-ous laboratories around the world.6,7 Reverse-transcriptase polymerase chain reaction (RT-PCR) tests, primarily tar-geting the polymerase gene of the virus, were developed, along with serologic tests for antibodies against the newly discovered SARS-CoV. Although these tests were made available during the outbreak, their sensitivity and speci-ficity were unknown because there were no “gold standard” laboratory or clinical definitions for the diagnosis of SARS. The formal disease definitions developed by the World
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".