Rapid Online Identification of Adverse Events After Influenza Immunization in Children by PCIRN’s National Ambulatory Network
Bibliographic record
Abstract
BACKGROUND: A National Ambulatory Network was created in 2009 to rapidly assess the safety of influenza vaccines. In 2012, the network was expanded to monitor the safety of live attenuated influenza vaccine and trivalent inactivated influenza vaccine in children. METHODS: We used an online survey administered 7 days after influenza immunization to track new or exacerbated health problems that required medical consultation or prevented daily activities. Parents of immunized children completed the survey. Reported adverse events were followed up by telephone within 48 hours of the online report. A sample of nonresponders was contacted by telephone to ensure the online responder group was representative. Event rates after the 2 influenza vaccines were compared. RESULTS: A total of 1230 parents completed an online or telephone survey, for a participation rate of 83%: 72% responded online and an additional 11% were reached by telephone. The rate of severe events in children immunized with an influenza vaccine was 4.7% (3.5-5.9%). The frequency and types of events reported were similar between online and telephone reports. Reported rates of severe events were similar after trivalent inactivated influenza or live attenuated influenza vaccine (4.0% vs. 5.1%, respectively). The online survey was easy to access and understand. Most respondents (94%) would participate next year. CONCLUSIONS: The rate and type of adverse events after immunization with trivalent inactivated influenza versus live attenuated influenza vaccine were similar and did not vary by reporting process (online vs. telephone). The electronic surveillance methodology provided rapid vaccine safety data in children. The electronic survey methodology was acceptable and feasible.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".