Enhanced passive safety surveillance of high-dose and standard-dose quadrivalent inactivated split-virion influenza vaccines in Germany and Finland during the 2022/23 influenza season
Bibliographic record
Abstract
Enhanced Passive Safety Surveillance (EPSS) was conducted for quadrivalent inactivated split-virion influenza vaccines (IIV4) in Germany (high dose [HD]) and Finland (standard dose [SD]) for the northern hemisphere (NH) 2022/23 influenza season. The primary objective was to assess adverse events following immunization (AEFI) occurring ≤7 days post-vaccination. In each country, the EPSS was conducted at the beginning of the NH influenza season. Exposure information was documented using vaccination cards (VC), and AEFI were reported via an electronic data collection system or telephone. AEFI were assessed by seriousness and age group (Finland only). The vaccinee reporting rate (RR) was calculated as the number of vaccinees reporting ≥ 1 AEFI divided by the total vaccinees. In Germany, among 1041 vaccinees, there were 31 AEFI (ten vaccinees) during follow-up, including one serious AEFI. Of 16 AEFI (six vaccinees) with reported time of onset, 15 occurred ≤7 days post-vaccination (RR 0.58%, 95% confidence interval [CI] 0.21, 1.25), which was lower than the 2021/22 season (RR 1.88%, 95% CI: 1.10, 3.00). In Finland, among 1001 vaccinees, there were 142 AEFI (51 vaccinees) during follow-up, none of which were serious. Of 133 AEFI (48 vaccinees) with time of onset reported, all occurred ≤7 days post-vaccination (RR 4.80%, 95% CI: 3.56, 6.31), which was similar to the 2021/22 season (RR 4.90%, 95% CI: 3.65, 6.43). The EPSS for HD-IIV4 and for SD-IIV4 in the 2022/23 influenza season did not suggest any clinically relevant changes in safety beyond what is known/expected for IIV4s.
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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 | 0.003 |
| 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 | 0.001 | 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".