Sensitivity and Diagnostic Yield of the First SARS-CoV-2 Nucleic Acid Amplification Test Performed for Patients Presenting to the Hospital
Bibliographic record
Abstract
Importance: Early and accurate diagnostic testing for SARS-CoV-2 is essential to initiate appropriate treatment and infection control and prevention measures among patients presenting to the hospital. Objective: To evaluate the diagnostic sensitivity of the SARS-CoV-2 nucleic acid amplification test (NAAT) performed within 24 hours of arrival to the emergency department among a nationally representative sample of patients. Design, Setting, and Participants: This diagnostic study was conducted at 47 hospitals across 7 provinces in Canada participating in the Canadian COVID-19 Rapid Response Emergency Department Network among consecutive eligible patients presenting to a participating emergency department who were tested for SARS-CoV-2 from March 1, 2020, to December 31, 2021. Patients not tested within 24 hours of arrival and those presenting with a positive result from a test performed in the community were excluded. Main Outcomes and Measures: The primary outcome was a positive result from the SARS-CoV-2 NAAT. Outcome measures were the diagnostic sensitivity and yield of the SARS-CoV-2 NAAT. Results: Of 132 760 eligible patients (66 433 women [50.0%]; median age, 57 years [IQR, 37-74 years]), 17 174 (12.9%) tested positive for SARS-CoV-2 within 14 days of their first NAAT. The diagnostic sensitivity of the SARS-CoV-2 NAAT was 96.2% (17 070 of 17 740 [95% CI, 95.9%-96.4%]) among all of the tests performed. Estimates ranged from a high of 97.7% (1710 of 1751 [95% CI, 96.8%-98.3%]) on day 2 of symptoms to a low of 90.4% (170 of 188 [95% CI, 85.3%-94.2%]) on day 11 of symptoms among patients presenting with COVID-19 symptoms. Among patients reporting COVID-19 symptoms, the sensitivity of the SARS-CoV-2 NAAT was 97.1% (11 870 of 12 225 [95% CI, 96.7%-97.3%]) compared with 87.6% (812 of 927 [95% CI, 85.2%-89.6%]) among patients without COVID-19 symptoms. The diagnostic yield of the SARS-CoV-2 NAAT was 12.0% (18 985 of 158 004 [95% CI, 11.8%-12.2%]) and varied from a high of 20.0% (445 of 2229 [95% CI, 18.3%-21.6%]) among patients tested on day 10 after symptom onset to a low of 8.1% (1686 of 20 719 [95% CI, 7.7%-8.5%]) among patients presenting within the first 24 hours of symptom onset. Conclusions and Relevance: This study suggests that the diagnostic sensitivity was high for the first SARS-CoV-2 NAAT performed in the hospital and did not vary significantly by symptom duration. Repeated testing of patients with negative test results should be avoided unless their pretest probability of disease is high.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".