Risk of Ruling out Severe Acute Respiratory Syndrome by Ruling in another Diagnosis: Variable Incidence of Atypical Bacteria Coinfection Based on Diagnostic Assays
Bibliographic record
Abstract
BACKGROUND: Severe acute respiratory syndrome (SARS) caused the first epidemic of the 21st century and continues to threaten the global community. OBJECTIVE: To assess the incidence of coinfection in patients confirmed to have SARS-associated coronavirus (SARS-CoV) infection, and thus, to determine the risk of ruling out SARS by ruling in another diagnosis. METHODS: The present report is a retrospective study evaluating the incidence and impact of laboratory-confirmed SARS-CoV and other pulmonary pathogens in 117 patients. These patients were evaluated in a Toronto, Ontario, community hospital identified as the epicentre for the second SARS outbreak. RESULTS: Coinfection with other pulmonary pathogens occurred in patients with SARS. Seventy-three per cent of the patient population evaluated had laboratory-confirmed SARS-CoV infection. Serology showing acute or recent Chlamydophila pneumoniae or Mycoplasma pneumoniae infection revealed an incidence of 30% and 9%, respectively, in those with SARS. These rates are similar to previously published studies on coinfection in pneumonia. All nucleic acid diagnostic assays were negative for C pneumoniae and M pneumoniae in respiratory samples from patients with SARS having serological evidence for these atypical pathogens. CONCLUSIONS: Diagnostic assays for well-recognized pulmonary pathogens have limitations, and ruling out SARS-CoV by ruling in another pulmonary pathogen carries significant risk. Despite positive serology for atypical pathogens, in a setting where clinical suspicion for SARS is high, specific tests for SARS should be performed to confirm or exclude a diagnosis.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".