Assessing safety of Ontario’s publicly funded MMR and MMRV immunization programs, 2012 to 2016
Bibliographic record
Abstract
BACKGROUND: The combined measles, mumps, rubella (MMR) and measles, mumps, rubella, and varicella (MMRV) vaccines are part of Ontario's routine immunization schedule. OBJECTIVE: To assess adverse events following immunization (AEFIs) reported in Ontario following administration of MMR and MMRV vaccines between 2012 and 2016. METHODS: Reports of AEFIs were extracted from the provincial surveillance database on May 9, 2017. Events were grouped by provincial surveillance definitions. Reporting rates were calculated using provincial population estimates or net doses distributed as the denominator. A serious AEFI is defined as an AEFI that resulted in an in-patient hospitalization or death. RESULTS: Overall, 289 AEFIs were reported following administration of MMR (n=246) or MMRV (n=43) vaccines, for annualized reporting rates of 16.6 and 8.8 reports per 100,000 distributed doses, respectively. The highest age-specific reporting rate was in children aged 1 to 3 years for MMR (7.7 per 100,000 population) and children aged 4 to 9 years for MMRV (0.8 per 100,000 population). Systemic reactions were the most frequently reported event category, while rash was the most frequently reported event for both vaccines. There were 22 serious AEFIs, 19 following MMR and 3 following MMRV (1.3 and 0.6 per 100,000 doses distributed, respectively). CONCLUSIONS: Our assessment found a low reporting rate of adverse events following MMR and MMRV vaccines in Ontario. No safety concerns were identified. Our findings are consistent with the safety profiles of these vaccines. Continued monitoring of vaccine safety is necessary to maintain timely detection of unusual postvaccine events and public confidence in vaccine safety.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".