The Role of Subgenomic RNA in Discordant Results From Reverse Transcription-Polymerase Chain Reaction Tests for COVID-19
Bibliographic record
Abstract
CONTEXT.—: Reverse transcription-polymerase chain reaction (RT-PCR) is the standard method of diagnosing COVID-19. An inconclusive test result occurs when 1 RT-PCR target is positive for SARS-CoV-2 and 1 RT-PCR target is negative for SARS-CoV-2 within the same sample. An inconclusive result generally requires retesting. One reason why a sample may yield an inconclusive result is that one target is at a higher concentration than another target. OBJECTIVE.—: To understand the role of subgenomic RNA transcripts in discordant results from RT-PCR tests for COVID-19. DESIGN.—: A panel of 6 droplet digital PCR assays was designed to quantify the ORF1, E-gene, and N-gene of SARS-CoV-2. This panel was used to quantify viral cultures of SARS-CoV-2 that were harvested during the eclipse phase and at peak infectivity. Eleven clinical nasopharyngeal swabs were also tested with this panel. RESULTS.—: In culture, infected cells showed higher N-gene/ORF1 copy ratios than culture supernatants. The same trends in the relative abundance of copies across different targets observed in infected cells were observed in clinical samples, although trends were more pronounced in infected cells. CONCLUSIONS.—: This study showed that a greater copy number of N-gene relative to E-gene and ORF1 transcripts could potentially explain inconclusive results for some RT-PCR tests on low viral load samples. The use of N-gene RT-PCR target(s) as opposed to ORF1 targets for routine testing is supported by these data.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".