2017/18 and 2018/19 seasonal influenza vaccine safety surveillance, Canadian National Vaccine Safety (CANVAS) Network
Bibliographic record
Abstract
BackgroundThe Canadian National Vaccine Safety (CANVAS) network monitors the safety of seasonal influenza vaccines in Canada.AimTo provide enhanced surveillance for seasonal influenza and pandemic influenza vaccines.MethodsIn 2017/18 and 2018/19 influenza seasons, adults (≥ 15 years of age) and parents of children vaccinated with the seasonal influenza vaccine participated in an observational study using web-based active surveillance. Participants completed an online survey for health events occurring in the first 7 days after vaccination. Participants who received the influenza vaccine in the previous season, but had not yet been vaccinated for the current season, were unvaccinated controls.ResultsIn 2017/18, 43,751 participants and in 2018/19, 47,798 completed the online safety survey. In total, 957 of 30,173 participants vaccinated in 2017/18 (3.2%; 95% confidence interval (CI): 3.0-3.4) and 857 of 25,799 participants vaccinated in 2018/19 (3.3%; 95% CI: 3.1-3.5) reported a health problem of sufficient intensity to prevent their normal daily activities and/or cause them to seek medical care (including hospitalisation). This compared to 323 of 13,578 (2.4%; 95% CI: 2.1-2.6) and 544 of 21,999 (2.5%; 95% CI: 2.3-2.7) controls in each respective season. The event rate in vaccinated adults and children was higher than the background rate and was associated with specific influenza vaccines. The higher rate of events was associated with systemic symptoms and migraines/headaches.ConclusionIn 2017/18 and 2018/19, higher rates of events were reported following seasonal influenza vaccination than in the pre-vaccination period. This signal was associated with several seasonal influenza vaccine products.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".