2017/18 and 2018/19 seasonal influenza vaccine safety surveillance, Canadian National Vaccine Safety (CANVAS) Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it