Multicenter Comparison of Two Norovirus ORF2-Based Genotyping Protocols
Bibliographic record
Abstract
Point source norovirus outbreaks can be difficult to track due to high background levels of the virus in the environment and the limited strain variation in some genotyping regions. However, rapid and accurate source identification can limit the spread of a foodborne outbreak and reduce the number of cases. Harmonization of genotyping assays is critical for enabling the rapid exchange of sequence data nationally and internationally. Several regions of the genome have been proposed for this purpose, but no consensus has been reached. In the present study, two standardized genotyping protocols (region C and region D) were evaluated by nine laboratories in Canada and the United States, using a coded panel of 96 fecal specimens representing 22 different norovirus genotypes. Overall, region C typing had a success rate of 78% compared to 52% for region D; however, region D provides greater nucleotide sequence diversity for identifying new GII.4 variant strains. Significant differences in the genotyping success rate were observed among the nine participating laboratories (10% to 100%) and among the different genotypes (6% to 100%). For several genogroup II strains, reduced region D amplification correlated directly with mismatches between primer sequences and the template. Based on overall performance, we recommend the region C protocol for routine genotyping of noroviruses, while the region D protocol may be useful for identifying new GII.4 variants. Standardized genotyping protocols will enable rapid exchange of outbreak and sequence data through electronic norovirus surveillance networks.
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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 | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".