An Outbreak of Human Coronavirus OC43 Infection and Serological Cross‐Reactivity with SARS Coronavirus
Bibliographic record
Abstract
BACKGROUND: In summer 2003, a respiratory outbreak was investigated in British Columbia, during which nucleic acid tests and serology unexpectedly indicated reactivity for severe acute respiratory syndrome coronavirus (SARS-CoV). METHODS: Cases at a care facility were epidemiologically characterized and sequentially investigated for conventional agents of respiratory infection, SARS-CoV and other human CoVs. Serological cross-reactivity between SARS-CoV and human CoV-OC43 (HCoV-OC43) was investigated by peptide spot assay. RESULTS: Ninety-five of 142 residents (67%) and 53 of 160 staff members (33%) experienced symptoms of respiratory infection. Symptomatic residents experienced cough (66%), fever (21%) and pneumonia (12%). Eight residents died, six with pneumonia. No staff members developed pneumonia. Findings on reverse transcriptase-polymerase chain reaction assays for SARS-CoV at a national reference laboratory were suspected to represent false positives, but this was confounded by concurrent identification of antibody to N protein on serology. Subsequent testing by reverse transcriptase-polymerase chain reaction confirmed HCoV-OC43 infection. Convalescent serology ruled out SARS. Notably, sera demonstrated cross-reactivity against nucleocapsid peptide sequences common to HCoV-OC43 and SARS-CoV. CONCLUSIONS: These findings underscore the virulence of human CoV-OC43 in elderly populations and confirm that cross-reactivity to antibody against nucleocapsid proteins from these viruses must be considered when interpreting serological tests for SARS-CoV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".