Enhanced passive safety surveillance of three marketed influenza vaccines in the UK and the Republic of Ireland during the 2017/18 season
Bibliographic record
Abstract
Safety surveillance is required for each season’s influenza vaccines to rapidly detect and evaluate potential new safety concerns before the peak period of immunization. Here we report the results of an enhanced passive safety surveillance for a trivalent split-virion inactivated influenza vaccine (IIV3; Vaxigrip®), an intradermal version of this vaccine (IIV3-ID; Intanza® 15 µg), and a recently licensed quadrivalent version (IIV4; VaxigripTetraTM) during the 2017/18 influenza season in the UK and Republic of Ireland. The primary objective was to determine the rates of adverse reactions (ARs) occurring within 7 days following routine vaccination. Between September and November 2017, 979 safety report cards were distributed to vaccinees receiving IIV3-ID, 1005 to those receiving IIV3, and 957 to those receiving IIV4. At least one AR was reported by 28 participants (2.9%) vaccinated with IIV3-ID, 14 participants (1.4%) vaccinated with IIV3, and 20 participants (2.1%) vaccinated with IIV4. The most frequent ARs were injection-site reactions and headache. One participant vaccinated with IIV3-ID reported two suspected serious ARs (dyskinesia and a shock symptom), although these could not be confirmed as vaccine-related. Rates of ARs for IIV3 and IIV3-ID for 2017/18 did not differ from the 2016/17 rates. For IIV4, in its first season since licensure, AR frequencies were similar to those in the Summary of Product Characteristics. In conclusion, no change was found compared to the known or expected AR rates for IIV3, IIV3-ID, or IIV4 during the 2017/18 season.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".