Recent advances in meningococcal B disease prevention: real-world evidence from 4CMenB vaccination
Bibliographic record
Abstract
OBJECTIVES: 4CMenB is a broadly protective vaccine against invasive meningococcal capsular group B disease (MenB IMD). Licensed worldwide based on immunogenicity and safety data, effectiveness and impact data are now available. We comprehensively reviewed all available real-world evidence gathered from use of 4CMenB since licensure. RESULTS: Data from 7 countries provide evidence of effectiveness and impact across different healthcare settings and age-groups, including national/regional immunization programs, observational studies and outbreak control. At least 2 4CMenB doses reduced MenB IMD by 50%-100% in 2-month to 20-year-olds depending on length of follow-up. Estimates of vaccine effectiveness in fully vaccinated cohorts ranged from 59%-100%. The safety profile of 4CMenB administered in real-world settings was consistent with pre-licensure clinical trial data. CONCLUSION: MenB IMD is an uncommon but life-threatening disease with unpredictable epidemiology. The substantial body of data demonstrating 4CMenB effectiveness and impact supports its use in IMD prevention. The results reinforce the importance of direct protection of the highest risk groups; infants/young children and adolescents. Direct protection via routine infant immunization with catch-up in young children and routine adolescent vaccination could be the preferred option for MenB disease control. A Video Abstract linked to this article is available on Figshare: https://doi.org/10.6084/m9.figshare.14546790.
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 | 0.033 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".