Seroprevalence of serum bactericidal antibodies to Neisseria meningitidis serogroup A in Burkina Faso
Bibliographic record
Abstract
Aim To describe the age-specific seroprevalence of serum bactericidal antibodies to Neisseria meningitidis serogroup A in Burkina Faso during the meningitis season of 2008 (non-epidemic year). Methods In February and March 2008, a representative sample of residents of urban Bobo-Dioulasso, Burkina Faso aged 0–59 years participated in a meningococcal seroprevalence (N=1008) and carriage (N=500) study. Serum bactericidal antibody (SBA) titres to strain F8238 (A:4:P1.20,9) were determined using standard methods (rabbit complement). Results The geometric mean titres (GMT) and prevalence of titres ≥8 varied substantially with age. In infants, only 4/107 (4%) had SBA titres >4, but 60% of 1-4 year olds had SBA titres ≥8. Both the GMTs and proportion with SBA titres ≥8 increased in each subsequent age group, peaking in 20-24 year olds (GMT = 488μg/ml, 90% with SBA titre ≥8) before declining in older adults. Provisional results indicate that overall meningococcal carriage prevalence was low (30 years [see abstract by Yaro et al.] The classic Goldschneider curves cannot be replicated in this population, with both high SBA titres and high (hyper-)endemic disease incidence being reported in individuals aged 5-29 years. The absence of detectable serogroup A carriage suggests that exposure to other organisms may induce serum bactericidal activity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".