Meningitis vaccines in children: what have we achieved and where next?
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review highlights the recent impacts of vaccines against the major bacterial causes of meningitis in children, and the challenges for further prevention of bacterial meningitis, with a focus on Streptococcus pneumoniae, Neisseria meningitidis and group B Streptococcus. RECENT FINDINGS: Conjugate vaccines against S. pneumoniae and N. meningitidis have resulted in dramatic reductions in bacterial meningitis globally where they have been used. Recent licensure and use of capsular group B meningococcal protein vaccines have further reduced meningococcal meningitis in infants, young children and adolescents for countries with endemic disease and during outbreaks. SUMMARY: Existing vaccines to prevent bacterial meningitis in children should be utilized in countries with significant numbers of cases of pneumococcal and/or meningococcal meningitis. Vaccines, which are able to protect against more than 13 serotypes of S. pneumoniae are in clinical trials and should be able to further reduce pneumococcal meningitis cases. Cost effective meningococcal vaccines against non-A capsular groups are needed for low-resource countries. There remains an urgent need for a vaccine against group B Streptococcus, which is a major cause of neonatal meningitis globally and for which no vaccine currently exists.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".