Assessing vaccine safety within Ontario’s Universal Influenza Immunization Program, 2012-2013 to 2014-2015
Bibliographic record
Abstract
BACKGROUND: Influenza vaccine is recommended to prevent influenza-related morbidity and mortality. Post-marketing surveillance of adverse events following influenza vaccine is essential to monitor vaccine safety, inform immunization program planning and evaluation, and build confidence in immunization. OBJECTIVE: To summarize adverse events following immunization (AEFIs) reported after receipt of influenza vaccines administered within the Universal Influenza Immunization Program in Ontario. METHODS: AEFIs following administration of influenza vaccines between September 1, 2012 and August 31, 2015 were extracted from the Integrated Public Health Information System (iPHIS) on September 1, 2015. Events were grouped by provincial surveillance definitions. Reporting rates were calculated using provincial population estimates or net doses distributed as the denominator. The standard World Health Organization definition of serious AEFIs was used. RESULTS: <0.05). The median age was 39.6 years (range six months-96 years); children under four years of age had the highest reporting rate (3.5 per 100,000 population). Disproportionate reporting among females was observed (76.5 percent), most notably in those 18 years and older. The most frequently reported events were injection site reactions (36.2 percent of reports). Others included allergic skin reactions (21.1 percent) and rashes (17.3 percent). Serious AEFIs were rare with a reporting rate of 1.6 per million doses distributed. CONCLUSION: This assessment found a low rate of reported adverse events following influenza vaccines administered in Ontario. Most reported events were mild and resolved completely. The findings were consistent with the very good safety profile of influenza vaccines.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Open science | 0.001 | 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".