Meningococcal B vaccination: real-world experience and future perspectives
Bibliographic record
Abstract
Invasive meningococcal disease (IMD) represents a severe risk for health. It can be considered the most dangerous vaccine-preventable disease due to the high probability of related permanent sequelae and death. The introduction in many countries of the conjugate vaccines against A, C, W135, and Y meningococcal serogroups influenced significantly the impact of the disease. Recently, the difficulties in obtaining an effective vaccine against meningococcal serogroup B (MenB) have been get over through the reverse vaccinology, enabling the recognition of some antigens providing a response against most of circulating MenB strains worldwide. The new 4cMenB vaccine is recommended in Europe, Canada, Australia, the USA, and some Latin American countries. Even if sound data on efficacy and safety profile are available, the results in terms of effectiveness are still limited. The management of the MenB outbreaks in two US universities demonstrated the ability to quickly achieve high vaccination coverage rates and no new cases among immunized subjects were assessed. It is desirable that the opportunity to complete preventive intervention against IMD offered by the new 4cMenB vaccine should be recognized and that this vaccine is included in the vaccination schedule to complete the panel of immunization against Neisseria meningitidis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".