Meningococcal disease in North America: Updates from the Global Meningococcal Initiative
Bibliographic record
Abstract
This review summarizes the recent Global Meningococcal Initiative (GMI) regional meeting, which explored meningococcal disease in North America. Invasive meningococcal disease (IMD) cases are documented through both passive and active surveillance networks. IMD appears to be decreasing in many areas, such as the Dominican Republic (2016: 18 cases; 2021: 2 cases) and Panama (2008: 1 case/100,000; 2021: <0.1 cases/100,000); however, there is notable regional and temporal variation. Outbreaks persist in at-risk subpopulations, such as people experiencing homelessness in the US and migrants in Mexico. The recent emergence of β-lactamase-positive and ciprofloxacin-resistant meningococci in the US is a major concern. While vaccination practices vary across North America, vaccine uptake remains relatively high. Monovalent and multivalent conjugate vaccines (which many countries in North America primarily use) can provide herd protection. However, there is no evidence that group B vaccines reduce meningococcal carriage. The coronavirus pandemic illustrates that following public health crises, enhanced surveillance of disease epidemiology and catch-up vaccine schedules is key. Whole genome sequencing is a key epidemiological tool for identifying IMD strain emergence and the evaluation of vaccine strain coverage. The Global Roadmap on Defeating Meningitis by 2030 remains a focus of the GMI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".