Improved post-marketing safety surveillance of quadrivalent inactivated influenza vaccine in Mexico using a computerized, SMS-based follow-up system
Bibliographic record
Abstract
Quadrivalent influenza vaccines (QIVs) are designed to prevent influenza disease caused by two influenza A viruses (H1N1 and H3N2) and both influenza B lineages. Risk-monitoring of QIVs to identify adverse events (AEs) is necessary as influenza vaccines are reformulated each year. We developed a new active surveillance system (Sistema de Control de Vacunación; SICOVA) to improve pharmacovigilance in Mexico. Participants (N = 2013) aged 0 − 96 years from nine sites across three influenza seasons (n = 1166 in 2015 − 2016; n = 633 in 2016 − 2017; and n = 214 in 2017 − 2018) agreed to receive text messages 1, 7, 28, and 42 days post-vaccination to know if they had experienced any AEs. The study was completed electronically by 1763 (87.6%) participants; manual follow-up was conducted for 250 participants whose reporting was incomplete. The overall AE rate was 9.09%. At least one AE was reported by 183 participants, of whom 131 (71.58%) did not require a medical visit and 52 (28.42%) needed medical attention, with none requiring hospitalization. Most AEs requiring medical attention occurred in children aged 0 − 5 years (n = 22, 42.31%) and adults aged 31 − 35 years (n = 5, 9.62%). These results are consistent with the established safety profile of Fluzone® Quadrivalent, and show that SICOVA can facilitate surveillance and increase AE reporting in Mexico.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".