Demonstrating the capacity of the National Advisory Committee on Immunization for timely responses to post-market vaccine monitoring signals: Canada’s experience with the live-attenuated influenza vaccine
Bibliographic record
Abstract
Over the last several years, the recommended use of the live-attenuated influenza vaccine (LAIV) for children has evolved in the United States (US) in response to evidence of a potential decrease in LAIV effectiveness based on post-market monitoring. These issues were not observed in Canada or elsewhere; consequently, recommendations from Canada's National Advisory Committee on Immunization (NACI) and the US Advisory Committee on Immunization Practices (ACIP) on whether to use LAIV differed for two influenza seasons (2016-2017 and 2017-2018). This retrospective describes how NACI arrived at its recommendations in response to post-market signals of reduced LAIV performance from the US in 2013-2014 and again in 2015-2016. NACI's experience with LAIV marks the first time in Canada where a preferential recommendation on the use of an influenza vaccine in a routine immunization program was reversed. This experience highlights the importance of ongoing post-market monitoring of vaccines, international collaboration and careful consideration of local context to inform vaccine recommendations. NACI's capacity for timely responses to post-market vaccine performance signals will facilitate responsiveness to similar post-market monitoring signals from the coronavirus disease 2019 (COVID-19) vaccines.
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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 | 0.035 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".