Electronic, self-reported active vaccine safety surveillance: A three-country comparison of AusVaxSafety (Australia), CANVAS (Canada) and V-safe (United States)
Bibliographic record
Abstract
Active vaccine safety surveillance, which involves directly engaging with vaccine recipients to actively solicit information on adverse events following immunization, complements spontaneous reporting systems. This commentary provides a comparative overview of three national active vaccine safety surveillance systems: AusVaxSafety (Australia), CANVAS (Canada) and V-safe (United States) across six key surveillance system evaluation attributes. Each system played an essential role during the COVID-19 pandemic and continues to contribute to global post-marketing vaccine safety surveillance. Each has various strengths and limitations. AusVaxSafety, established in 2014, is well integrated with routine healthcare settings for opt-out surveillance and offers frequent public reporting. CANVAS, established in 2009, offers a unique cohort-based design with inclusion of control groups. V-safe, established in 2020, has proven scalability and broad population reach due to its accessible web-based design. The various features of each system offer insights to inform future vaccine safety surveillance efforts.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".