Evaluation of 5 Polymerase Chain Reaction Assays for the Detection of Mpox Virus
Bibliographic record
Abstract
BACKGROUND: In 2022, the global dissemination of mpox virus (MPXV) outside endemic regions prompted the expansion of diagnostic testing worldwide. This study assesses the performance characteristics of 5 real-time polymerase chain reaction (PCR) assays in detecting MPXV during the 2022 outbreak. METHODS: Clinical specimens collected from patients across Ontario, Canada, were tested on the following assays: RealStar Orthopoxyvirus PCR and FlexStar Monkeypox virus PCR (Altona Diagnostics), Novaplex MPXV (Seegene), VIASURE Monkeypox virus Real Time PCR Reagents (CerTest Biotec), and a laboratory-developed test. Positive percent agreement (PPA), negative percent agreement (NPA), relative limit of detection (LOD), and precision were evaluated and MPXV lineages were determined using an amplicon-based whole-genome sequencing (WGS) assay. RESULTS: Swabs were collected from various anatomic sites (65 positive and 30 negative). All assays demonstrated 100% NPA (95% confidence interval, 88.4%/88.1%-100.0%), with PPA ranging from 92.2% (82.7%-97.4%) to 96.9% (89.3%-99.6%). LOD and precision were comparable across assays, with coefficient of variations <3%. WGS analysis identified 6 lineages, all belonging to subclade IIb. CONCLUSIONS: The assays exhibited excellent PPA, NPA, LOD, and precision. Ongoing performance monitoring is essential to detect assay escape mutants and ensure universal detection of evolving MPXV strains.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".