Enhanced passive safety surveillance of standard-dose and high-dose influenza vaccines in Finland and Germany 2023–24 season
Bibliographic record
Abstract
Enhanced Passive Safety Surveillance was used to detect safety signals before the peak period of immunization with quadrivalent inactivated influenza vaccines (IIV4) in Finland (standard dose [SD]) and Germany (high dose [HD]) in the 2023-24 season. The primary objective was to evaluate adverse drug reactions (ADRs) occurring ≤7 days following IIV4 vaccination. Enrolled participants were vaccinated in routine clinical care settings and encouraged to report ADRs. Exposure data and ADR reports were collected in a near real-time manner using an electronic system. Vaccinee reporting rate (RR) with 95% confidence interval (CI) was calculated as the number of vaccinees reporting ≥ 1 ADR divided by total number of vaccinees. In Finland for SD-IIV4, among 1,003 vaccinees aged ≥ 6 months, 81 reported a total of 192 suspected ADRs occurring ≤ 7 days following vaccination (vaccinee RR 8.08%; 95% CI 6.46, 9.94). In Germany for HD-IIV4, among 1,075 vaccinees aged ≥ 60 years, 15 reported 46 ADRs that occurred in ≤ 7 days of vaccination (vaccinee RR 1.40%; 95% CI 0.78, 2.29). No safety signal was detected during this surveillance. The 2023-24 season surveillance did not suggest any clinically significant changes in safety profile compared with previously reported safety data for SD-IIV4 and HD-IIV4.
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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 | 0.003 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".