Challenges in Interpretation of Diagnostic Test Results in a Mumps Outbreak in a Highly Vaccinated Population
Bibliographic record
Abstract
In spite of a greatly reduced incidence rate due to vaccination, mumps outbreaks continue to occur in several areas of the world, sometimes in vaccinated populations. This article describes an outbreak in a highly vaccinated population in southwestern Ontario, Canada, and the challenges encountered in interpreting the results of diagnostic tests used in the outbreak. During the outbreak, patients were interviewed and classified according to the outbreak case definition, and specimens were collected for diagnostic testing according to Ontario guidelines. Twenty-seven individuals were classified as confirmed cases (n = 19) or suspect cases (n = 8) according to the case definition, only 9 of which were laboratory-confirmed cases: 7 confirmed by reverse transcriptase PCR (RT-PCR) and 2 by IgM serology. All 19 confirmed cases represented patients who were associated with secondary schools in the local area and had been vaccinated against mumps with one (n = 2) or two (n = 17) doses of the measles-mumps-rubella (MMR) vaccine. This is the first published report of an outbreak of mumps in Ontario in which all confirmed cases had been vaccinated against the disease. It highlights the limitations of and difficulties in interpreting current mumps diagnostic tests when used in vaccinated individuals.
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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 | 0.045 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".