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 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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".