High Genetic Variability of Norovirus Leads to Diagnostic Test Challenges
Bibliographic record
Abstract
Abstract Background It is important to understand the diagnostic accuracy of syndromic multiplex panels such as the Luminex xTAG® Gastrointestinal Pathogen Panel (GPP) as they are increasingly employed as routine diagnostic tests in laboratories worldwide. Recent evaluations in our laboratory identified lower detection rates of norovirus genogroup II (NoV GII) using the GPP as compared with our lab-developed RT-qPCR Gastroenteritis Virus Panel (GVP). This study is to characterize the NoV strains in samples with discordant NoV GII test results between GPP and GVP and determine the sensitivity of the two assays for specific NoV GII genotypes. Methods We genotyped all NoV GII strains with discordant test result in stool samples or rectal swabs collected prospectively from a cohort of children with acute gastroenteritis between December 2014 and July 2016. The sensitivity of GVP and GPP for NoV GII were compared by analyzing GVP threshold cycle (Ct) and using ten-fold serial dilutions of positive samples of various NoV GII genotypes. Results All discordant samples (11%; 63/607) tested positive for NoV GII by GVP but negative by GPP. Thirty-five percent (22/63) were successfully genotyped; 64% (14/22) of those were NoV GII genotype 2 (GII.2). The median Ct value of concordant positive was lower than those with discordant results (19.8 vs.. 33.7 respectively; P < 0.0001). GVP was 10-fold and at least 10,000-fold more sensitive than GPP in detecting NoV GII.3 and GII.2, respectively, but has similar sensitivity for NoV GII.4. The GII.2 variants with discordant test results differed genetically from the concordant GII.2 variants. Conclusion GPP has suboptimal sensitivity to detect NoV GII.2 and its use may lead to an underestimation of NoV disease burden with some cases not being detected. Disclosures All authors: No reported disclosures.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".