Diagnostic Yield of Laboratory Methods and Value of Viral Genotyping during an Outbreak of Mumps in a Partially Vaccinated Population in British Columbia, Canada
Bibliographic record
Abstract
ABSTRACT Mumps remains endemic in North America despite routine use of the measles, mumps, and rubella (MMR) vaccine. In 2016, an outbreak of mumps in British Columbia, Canada, provided an opportunity to determine the diagnostic utility of laboratory testing methods. Specimens from patients with clinical mumps were tested for infection using a commercial enzyme-linked immunosorbent assay (ELISA) for antibody detection and an in-house reverse transcriptase PCR (RT-PCR) targeting viral fusion and small hydrophobic (SH) genes. Viral genotyping was performed by SH gene sequencing. Laboratory data was linked with epidemiologic case data. Of the 139 confirmed cases, 94 (68%) had reported or documented history of MMR vaccination. Specimens were typically collected 1 day (for buccal and IgM tests) or 2 days (for urine tests) after symptom onset. Most confirmed cases (69%) were confirmed by buccal swab RT-PCR. Among cases tested by multiple methods, the percent positivity for buccal swab RT-PCR was 90% (96/107) compared to 43% (30/69) for both IgM ELISA and urine RT-PCR. Mumps IgM detection was higher in confirmed cases with no history of vaccination than in those with history (64% versus 34%, P = 0.02). The outbreak strain was identified as genotype G related to MuVi/Sheffield.GBR/1.05 but with conserved variations in five nucleotides within the SH gene that allowed linkage of geographically distinct cases. In conclusion, RT-PCR of buccal specimens had the highest diagnostic yield during a mumps outbreak in a partially vaccinated population. To optimize mumps diagnostic potential, clinicians should collect specimens depending on when the patient presents for care and their immunization history.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".