Correlation Between the COVID-19 Respiratory Triage Score and SARS-COV-2 PCR Test
Bibliographic record
Abstract
Background: COVID-19 clinical presentation is usually non-specific and includes commonly encountered symptoms like fever, cough, nausea, and vomiting. It has been reported that COVID-19 patients can potentially transmit the disease to others before developing symptoms. Thus, extensive surveillance and screening of individuals at risk of the disease is required to limit SARS-COV-2 spread. The COVID-19 respiratory triage score has been used for patient screening. We aimed to determine its diagnostic performance characteristics, which have not been adequately studied before. Methodology: This is a retrospective observational study involving all patients screened for COVID-19 at a tertiary care facility. Patients were tested using nasopharyngeal swab for SARS-COV-2 PCR. The Saudi CDC COVID-19 respiratory triage score was measured for all subjects. The sensitivity, specificity, positive predictive value, and negative predicted value of COVID-19 respiratory triage score were measured with reference to SARS-COV-2 PCR test. Multivariate regression analysis was done to identify factors that can predict a positive SARS-COV-2 PCR test. Result: A total of 1,435 subjects were included. The COVID-19 respiratory triage score provided a marginal diagnostic performance with a receiver-operating characteristics (ROC) area under the curve value of 0.60 (95% CI: 0.57–0.64). A triage score of 5 provided the best cut-off value for the combined sensitivity and specificity. Clinical characteristics that independently predicted positive COVID-19 PCR test include male sex (adjusted OR: 1.47; p = 0.034), healthcare workers and their family members (adjusted OR: 1.99; 95%; p = 0.016), fever (adjusted OR: 2.98; p < 0.001), and moderate disease severity (adjusted OR: 5; p < 0.001). Conclusion: The current COVID-19 respiratory triage score has marginal diagnostic performance characteristics. Its performance can improve by including additional predictors to the respiratory symptoms in order to avoid missing COVID-19 patients with atypical presentation and to limit unnecessary SARS-COV-2 PCR testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.176 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".