Performance of MassARRAY system for the detection of SARS-CoV-2 compared to real-time PCR
Bibliographic record
Abstract
Background: Early identification of COVID-19 (coronavirus disease of 2019) by diagnostic tests played an important role in the isolation of infectious patients and management of this pandemic. Various methodologies and diagnostic platforms are available. The current "gold standard" for SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) diagnosis is real-time reverse transcriptase-polymerase chain reaction (RT-PCR). To overcome the limitations posed by the short supply experienced early during the pandemic and to increase our capacity, we assessed the performance of the MassARRAY System (Agena Bioscience). Methods: MassARRAY System (Agena Bioscience) combines RT-PCR (reverse transcription-polymerase chain reaction) with high-throughput mass spectrometry processing. We compared the MassARRAY performance to a research-use-only E-gene/EAV (Equine Arteritis Virus) assay and RNA Virus Master PCR. Discordant results were tested with a laboratory-developed assay using the Corman et al. E-gene primers and probes. Results: 186 patient specimens were analyzed using the MassARRAY SARS-CoV-2 Panel. The performance characteristics were as follows: the positive agreement was 85.71%, 95% CI (78.12 - 91.45), and the negative agreement was 96.67%, 95% CI (88.47 - 99.59). 19/186 (10.2%) results were found to be discordant and assessed by a different assay with the exception of 1, where the sample was not available for repeat testing. 14 out of 18 agreed with the MassARRAY after testing with the secondary assay. The overall performance after discordance testing was as follows: the positive agreement was 97.3%, 95% CI (90.58 - 99.67), and the negative agreement was 97.14%, 95% CI (91.88 - 99.41). Conclusion: Our study demonstrates that the MassARRAY System is an accurate and sensitive method for SARS-CoV-2 detection. Following the discordant agreement with an alternate RT-PCR test, the performance was found to have sensitivity, specificity, and accuracy exceeding 97%, making it a viable diagnostic tool. It can be used as an alternative method during periods when real-time RT-PCR reagent supply chains are disrupted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".