Understanding the low effectiveness of influenza vaccines in older adults of South Korea: an exploration of contributing factors
Bibliographic record
Abstract
Despite achieving high influenza vaccine coverage (>80 %) among older adults, South Korea consistently reports lower influenza vaccine effectiveness (VE) compared to the United States, Canada, and Europe. This viewpoint reviews the potential causes of low VE, highlighting antigenic dissimilarity, immune imprinting, egg-adapted vaccine effects, and low natural exposure. Historical data records frequent antigenic dissimilarities in South Korea, contributing to reduced VE. Additionally, annual vaccination may lead to immune imprinting, potentially limiting effective antibody responses against antigenically distinct strains. Egg-based vaccine production further skews immune responses toward egg-adapted epitopes. In settings with mature vaccination programs, limited natural influenza exposure may paradoxically result in lower observed VE. To address these challenges, we propose strategies including enhanced regional influenza surveillance in global vaccine strain selection, adoption of non-egg-based platforms (mRNA and cell culture vaccines), highly immunogenic vaccines and immuno-focusing techniques. These approaches hold promise for improving future influenza VE in South Korea and globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".