Vaccine safety surveillance in Canada: Reports to CAEFISS, 2017
Bibliographic record
Abstract
BACKGROUND: Canada has a comprehensive vaccine safety surveillance system that includes both passive and active surveillance of vaccines administered in Canada. OBJECTIVES: To provide 1) a descriptive analysis of the adverse events following immunization (AEFI) reports for vaccines administered in Canada, 2) a descriptive review of health care utilization and outcome following an AEFI and 3) an analysis of serious adverse events (SAEs). METHODS: Data was obtained from the Canadian Adverse Events Following Immunization Surveillance System (CAEFISS), which includes both passive and active surveillance. Descriptive analyses were conducted of AEFI reports arising from vaccines administered from January 1, 2017 to December 31, 2017 and received by April 30, 2018. Data elements included AEFIs, demographics, health care utilization, outcome, and seriousness of adverse events. RESULTS: There were 2,960 AEFI reports submitted to CAEFISS from across Canada for vaccines administered in 2017. The AEFI reporting rate was 12.6/100,000 doses distributed (8.1/100,000 population) in Canada for vaccines administered in 2017 and was found to be inversely proportional to age. The majority of reports (91%) were non-serious events, primarily involving vaccination site reactions such as rash, and allergic events. Overall, there were 253 SAE reports, for a reporting rate of 1.1/100,000 doses distributed in 2017. Of the SAE reports, the most common primary AEFIs were seizure (n=58, 23%), followed by anaphylaxis (n=33, 13%). There were no unexpected vaccine safety issues identified or increases in frequency or severity of expected adverse events. CONCLUSION: Canada's continuous monitoring of the safety of marketed vaccines in 2017 did not identify any increase in the frequency or severity of AEFIs, previously unknown AEFIs or areas that required further investigation or research. Vaccines marketed in Canada continue to have an excellent safety profile.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".