Analytical and Clinical Evaluation of Two RT-qPCR SARS-CoV-2 Diagnostic Tests with Emergency Use Authorization in Ecuador
Bibliographic record
Abstract
Dozens of RT-qPCR kits are available in the market for SARS-CoV-2 diagnosis, some of them with Emergency Use Authorization (EUA) by the Food and Drug Administration (FDA) or at least by a responsible agency of their country of origin, but many of them lack proper evaluation studies because of COVID-19 pandemic emergency. We evaluated the clinical performance of two commercially available kits in South America, the 2019-nCoV kit (Da An Gene, Guangzhou, China) and GenomeCoV19 kit (ABM, Richmond, Canada), for RT-qPCR SARS-CoV-2 diagnosis using the FDA EUA 2019-nCoV CDC kit (IDT, Coralville, IA) as gold standard. We found striking differences among clinical performance and analytical sensitivity in both kits; whereas the 2019-nCoV kit (Da An Gene) has a limit of detection of 2,000 copies/mL and 100% of sensitivity, the GenomeCoV19 kit (ABM) has a poor sensitivity of 75% and a limit of detection estimated to be over 8.000 copies/mL. The GenomeCoV19 kit (ABM) lacks clinical use authorization in Canada; however, the 2019-nCoV kit (Da An Gene) is authorized by the Chinese CDC. Our results support that only SARS-CoV-2 diagnosis kits with clinical use authorization from their country of origin should be exported to developing countries lacking proper evaluation agencies to avoid a deep impact of the COVID-19 pandemic due to unreliable 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".