The Canadian National Vaccine Safety (CANVAS) network: Cross-sectional analysis of seasonal influenza vaccine safety in children during the 2013/2014 to 2019/2020 influenza seasons
Bibliographic record
Abstract
The Canadian National Vaccine Safety (CANVAS) network is a participant-centered, active surveillance system that assesses vaccine safety across Canada. Our study examined the safety profile of influenza vaccines administered to children during seven consecutive influenza seasons, from 2013/2014 to 2019/2020, to establish a baseline for health events in vaccinated and unvaccinated children before the COVID-19 pandemic. Data were collected using an online survey after a 7-day period for vaccinated and unvaccinated participants. Descriptive and inferential analyses explored the association between the influenza vaccine and health events, including associated symptoms. The proportion of health events were comparable in vaccinated and unvaccinated children. Adjusted regression models identified no statistical difference in the occurrence of health events assessed. The most frequently reported symptoms were fever, gastrointestinal symptoms (i.e., nausea/vomiting/diarrhea), and cough. Our findings observed low incidence of health events in both vaccinated and unvaccinated groups in this multi-year study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".