Bibliographic record
Abstract
BACKGROUND: An increase in mumps incidence was observed in late 2016 (365 cases in 2016 compared to 59 cases in 2015). This unusual level of mumps activity prompted the Public Health Network Council and the National Advisory Committee on Immunization to request situation awareness updates from the Centre for Immunization and Respiratory Infectious Diseases (CIRID) at the Public Health Agency of Canada in 2017 and 2018. METHODS: A mumps outbreak survey was developed and administered by epidemiologists within CIRID and sent electronically to provincial and territorial public health officials in charge of mumps surveillance. The survey collected information on mumps outbreaks pertaining to demographics, risk factors, laboratory data and public health interventions. The first survey collected data on outbreaks occurring between January 1, 2016 and February 28, 2017, while the second survey contained outbreak data from January 1, 2017 to July 31, 2018. Duplicate outbreaks entries were removed. RESULTS: The response rate for the first and second surveys was 61% and 69%, respectively. Twenty-four mumps outbreaks across nine provinces were reported between January 1, 2016 and July 31, 2018, for a cumulative total of 881 mumps cases. Adolescents and adults 15 to 39 years of age accounted for the majority of cases (80.6%). Specifically, adults 20 to 24 years of age represented the largest proportion of cases (24.6%). Community and social gatherings were the most common exposure setting (62.5%). Slightly more than one third of cases were known to have received at least two doses of mumps-containing vaccine (35.6%). CONCLUSION: Results from the surveys indicate that the increase in mumps activity was widespread throughout Canada, affecting multiple jurisdictions. Young adults accounted for the largest proportion of cases. These surveys provided evidence to support recommendations on the use of additional mumps vaccination in outbreak settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".