Laboratory Diagnosis of Mumps in a Partially Immunized Population: The Nova Scotia Experience
Bibliographic record
Abstract
BACKGROUND: In 2007, Atlantic Canada experienced a large outbreak of mumps predominately in university students who had received a single dose of measles, mumps and rubella vaccine. The present study describes the performance characteristics of reverse transcriptase polymerase chain reaction (RT-PCR) on buccal and urine specimens and immunoglobulin M (IgM) serology in this partially immune population. METHODS: Patients presenting with symptoms suspicious for mumps had a serum, urine and a buccal swab collected for diagnostic testing. Persons were classified as a 'confirmed' case according to the Public Health Agency of Canada's definition. Sera were tested using an enzyme-linked immunoassay. Detection of mumps virus in buccal swabs and urine samples was performed by RT-PCR. RESULTS: A subset of 155 cases and 376 non-cases that had all three specimens submitted was used for calculating the performance characteristics. The sensitivity of RT-PCR on buccal swabs, urine specimens and IgM serology were 79%, 43% and 25%, respectively. The specificity of RT-PCR on buccal swabs, urine specimens and IgM serology was 99.5%, 100% and 99.7%, respectively. Only 12 of 134 (9%) patients had positive urine specimens in the presence of negative oral swabs. CONCLUSION: RT-PCR on buccal swabs is the ideal specimen for diagnosis. Testing an additional urine sample in an outbreak setting did not increase the diagnostic yield significantly, but doubled testing volume and cost. In addition, the data suggest that, in this partially immune group, IgM serology has little value in the diagnosis of acute infection.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".