Development and validation of RT-PCR assays for testing for SARS-CoV-2
Bibliographic record
Abstract
Background: The recent emergence and rapid global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) demonstrates the urgent need for laboratory-developed assays for clinical diagnosis and public health interventions in the absence of commercial assays. Methods: We outline the progression of reverse-transcriptase polymerase chain reaction (RT-PCR) assays that were developed and validated at the Alberta Precision Laboratories, Public Health Laboratory, Alberta, Canada, to respond to this pandemic. Initially, testing was performed using SARS-CoV-2-specific and pan-coronavirus gel-based assays that were soon superseded by real-time RT-PCR assays targeting the envelope and RNA-dependent RNA polymerase genes to accommodate the high anticipated volumes of samples. Throughput was further enhanced by multiplexing the different targets together with the co-detection of an internal extraction control. Results: These assays are comparable in sensitivity and specificity to the assays recommended by the World Health Organization and the US Centers for Disease Control and Prevention. Conclusions: The availability of real-time RT-PCR assays early in the pandemic was essential to provide valuable time to local health authorities to contain transmission and prepare for appropriate response strategies.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".