The Global Meningococcal Initiative meeting on prevention of meningococcal disease worldwide: Epidemiology, surveillance, hypervirulent strains, antibiotic resistance and high-risk populations
Bibliographic record
Abstract
INTRODUCTION: The 2018 Global Meningococcal Initiative (GMI) meeting focused on evolving invasive meningococcal disease (IMD) epidemiology, surveillance, and protection strategies worldwide, with emphasis on emerging antibiotic resistance and protection of high-risk populations. The GMI is comprised of a multidisciplinary group of scientists and clinicians representing institutions from several continents. AREAS COVERED: Given that the incidence and prevalence of IMD continually varies both geographically and temporally, and surveillance systems differ worldwide, the true burden of IMD remains unknown. Genomic alterations may increase the epidemic potential of meningococcal strains. Vaccination and (to a lesser extent) antimicrobial prophylaxis are the mainstays of IMD prevention. Experiences from across the globe advocate the use of conjugate vaccines, with promising evidence growing for protein vaccines. Multivalent vaccines can broaden protection against IMD. Application of protection strategies to high-risk groups, including individuals with asplenia, complement deficiencies and human immunodeficiency virus, laboratory workers, persons receiving eculizumab, and men who have sex with men, as well as attendees at mass gatherings, may prevent outbreaks. There was, however, evidence that reduced susceptibility to antibiotics was increasing worldwide. EXPERT COMMENTARY: The current GMI global recommendations were reinforced, with several other global initiatives underway to support IMD protection and prevention.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".