Successful methodology for large-scale surveillance of severe events following influenza vaccination in Canada, 2011 and 2012
Bibliographic record
Abstract
In 2011 and 2012, a nationwide Canadian vaccine safety surveillance network rapidly collected safety data from healthcare workers (HCW) during the first weeks of the annual influenza vaccination campaign. This network provided the first available post-marketing safety data on seasonal influenza vaccines with information on background rates as a comparator. In 2012, these data were used to investigate a possible safety concern regarding a particular vaccine. An online questionnaire was provided to participating HCW two weeks before the annual influenza vaccination campaign for controls, and eight days after influenza vaccination for vaccinees. Control and vaccinees were requested to report health events occurring in the seven days prior to receiving the questionnaire. Control data were used to calculate background rates. HCW reporting a severe event were followed-up by telephone within 48 hours of the online report to validate the report and check on their health status. More than 22,000 vaccinated HCW were enrolled and surveyed over two seasons and > 90% reported no severe event following vaccination. Validated severe event rates were similar in vaccinated HCW and unvaccinated HCW (2.2% vs 2.3%; p < 0.70). The questionnaire was accurately completed for most reported symptoms, matched the validated report and was able to detect events of interest. Prior to the safety concern, the implicated vaccine was in use at one centre. Reassuring safety data were provided to public health authorities 48 hours after the vaccine was temporarily suspended. Data from this and similar networks can be used for rapid evaluation of vaccine safety and for safety assessment as required by the European Medicines Agency in 2015.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".