Testing the limits of multiplex respiratory virus assays for SARS-CoV-2 at high cycle threshold values: Comparative performance of cobas 6800/8800 SARS-CoV-2 & Influenza A/B, Xpert Xpress SARS-CoV-2/Flu/RSV, and cobas Liat SARS-CoV-2 & Influenza A/B
Bibliographic record
Abstract
Background: Multiplex real-time RT-PCR assays for respiratory pathogens are valuable tools to optimize laboratory workflow and turnaround time. At a time when resurgence of influenza and respiratory syncytial virus (RSV) cases have been widely observed along with continued transmission of SARS-CoV-2, timely identification of all circulating respiratory viruses is crucial. This study evaluates the detection of low viral loads of SARS-CoV-2 by four multiplex molecular assays: Roche cobas 6800/8800 SARS-CoV-2 & Influenza A/B Test, Cepheid Xpert Xpress SARS-CoV-2/Flu/RSV, cobas Liat SARS-CoV-2 & Influenza A/B, and a laboratory-developed test (LDT). Methods: Retrospective upper respiratory tract specimens positive for various respiratory viruses at a range of cycle threshold (Ct) values (18-40) were tested by four multiplex assays. Positive and negative percent agreement (PPA and NPA) with validated RT-PCR assays were calculated. Results: A total of 82 samples were assessed, with discordant results observed in a portion of the samples (10/82, 12.2%) where Ct values were >33. The majority of the discordant results (6/10, 60%) were false negatives. Overall, PPA was 100% (58/58) for cobas 6800, 97.4% (38/39) for GeneXpert, 100% (17/17) for Liat, and 90.5% (57/63) for the LDT. PPA for the LDT increased to 92.1% after manual review of amplification curves. Conclusions: Commercial multiplex respiratory virus assays have good performance for samples with medium to high viral loads (Ct values <33). Laboratories should consider appropriate test result review and confirmation protocols to optimize sensitivity, and may consider reporting samples with additional interpretive comments when low viral loads are detected.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".