Listeria monocytogenes meningitis in the Netherlands, 1985–2014: A nationwide surveillance study
Bibliographic record
Abstract
OBJECTIVES: Listeria monocytogenes can cause sepsis and meningitis. We report national surveillance data on L. monocytogenes meningitis in the Netherlands, describing incidence changes, genetic epidemiology and fatality rate. METHODS: We analyzed data from the Netherlands Reference Laboratory of Bacterial Meningitis for cases of L. monocytogenes meningitis. Strains were assessed by serotyping and bacterial population structure by multi-locus sequence typing. RESULTS: A total of 375 cases of Listeria meningitis were identified between 1985 and 2014. Peak incidence rates were observed in neonates (0.61 per 100,000 live births) and older adults (peak at 87 year; 0.53 cases per 100,000 population of the same age). Neonatal listerial meningitis decreased 17-fold from 1.95 per 100,000 live births between 1985 and 1989, to 0.11 per 100,000 live births between 2010 and 2014. Overall case fatality rate was 31%, in a multivariate analysis older age and concomitant bacteremia were associated with mortality (both p < 0.01). Clonal complexes (CC) CC1, CC2 and CC3 decreased over time from respectively 32% to 12%, 33% to 9% and 10% to 2% (all p < 0.001), while CC6 increased from 2% to 26% (p < 0.001). CONCLUSIONS: The incidence of neonatal listerial meningitis has declined over the past 25 years. The genotype CC6 has become the predominant genotype in listerial meningitis in the Netherlands. Mortality of listeria meningitis has remained high.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".