Clinical assessment of SARS-CoV-2 antigen rapid detection compared with RT-PCR assay for emerging variants at a high-throughput community testing site in Taiwan
Bibliographic record
Abstract
OBJECTIVES: With the emergence of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) B.1.1.7 lineage in the ongoing coronavirus disease 2019 (COVID-19) pandemic, Taiwan confronted a COVID-19 flare up in May 2021. Large-scale, accurate, affordable and rapid diagnostic tests such as the lateral flow assay can help to prevent community transmission, but their performance characteristics in real-world conditions and relevant subpopulations remain unclear. METHODS: The COVID-19 Antigen Rapid Test Kit (Eternal Materials, New Taipei City, Taiwan) was used in a high-throughput community testing site; the paired reverse transcription polymerase chain reaction (RT-PCR) results served as a reference for sensitivity and specificity calculations. RESULTS: Of 2096 specimens tested using the rapid antigen test, 70 (3.33%) were positive and 2026 (96.7%) were negative. This clinical performance was compared with the RT-PCR results. The sensitivity and specificity of the rapid antigen test were 76.39% [95% confidence interval (CI) 64.91-85.60%] and 99.26% (95% CI 98.78-99.58%), respectively, with high sensitivity in subjects with cycle threshold values ≤24. Further, the rapid antigen test detected the SARS-CoV-2 B.1.1.7 lineage effectively. CONCLUSIONS: Considering the short turnaround times and lower costs, this simple SARS-CoV-2 antigen detection test for rapid screening combined with RT-PCR as a double confirmatory screening tool can facilitate the prevention of community transmission during COVID-19 emergencies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".