A critical appraisal of the recommendations for the use of meningococcal conjugate vaccines
Bibliographic record
Abstract
OBJECTIVES: To assess the epidemiology of meningococcal disease (MD) in Brazil and the impact that recent evidence and lessons learned from the introduction of meningococcal C conjugate (MCC) vaccines into immunization programs may have on different strategies of vaccine use. SOURCES: Non-systematic review of the MEDLINE, SciELO and LILACS databases covering the period from 2000 to 2011. SUMMARY OF THE FINDINGS: Meningococcal disease is endemic in Brazil, with periodic occurrence of outbreaks. Most cases are associated with serogroup C and the highest incidence rates are observed in infants, encouraging the introduction of MCC vaccine in the National Immunization Program in 2010 for children under 2 years old. The introduction of MCC vaccines into immunization programs in Europe, Canada and Australia proved to be effective, with dramatic reduction in the incidence of serogroup C meningococcal disease, not only in the vaccinated, but also in the unvaccinated individuals. Long-term effectiveness of MCC vaccines was dependent on a combination of antibody persistence, immunologic memory and herd protection. Recent evidence indicating that antibody persistence is not long-lasting in young immunized children, and that immunologic memory is not fast enough to protect them against the disease, emphasize the importance of herd protection to maintain the population protected. CONCLUSIONS: The rapid decline of antibody titers in children vaccinated in the first years of life suggests the need to incorporate booster doses before adolescence, especially in locations like Brazil, where the immunization program did not incorporate catch-up campaigns including adolescents, lacking the herd immunity effect.
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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 | 0.071 | 0.309 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".