Paediatric meningitis in the conjugate vaccine era and a novel clinical decision model to predict bacterial aetiology
Bibliographic record
Abstract
OBJECTIVES: The aims of this study were to assess aetiology and clinical characteristics in childhood meningitis, and develop clinical decision rules to distinguish bacterial meningitis from other similar clinical syndromes. METHODS: Children aged <16 years hospitalised with suspected meningitis/encephalitis were included, and prospectively recruited at 31 UK hospitals. Meningitis was defined as identification of bacteria/viruses from cerebrospinal fluid (CSF) and/or a raised CSF white blood cell count. New clinical decision rules were developed to distinguish bacterial from viral meningitis and those of alternative aetiology. RESULTS: The cohort included 3002 children (median age 2·4 months); 1101/3002 (36·7%) had meningitis, including 180 bacterial, 423 viral and 280 with no pathogen identified. Enterovirus was the most common pathogen in those aged <6 months and 10-16 years, with Neisseria meningitidis and/or Streptococcus pneumoniae commonest at age 6 months to 9 years. The Bacterial Meningitis Score had a negative predictive value of 95·3%. We developed two clinical decision rules, that could be used either before (sensitivity 82%, specificity 71%) or after lumbar puncture (sensitivity 84%, specificity 93%), to determine risk of bacterial meningitis. CONCLUSIONS: Bacterial meningitis comprised 6% of children with suspected meningitis/encephalitis. Our clinical decision rules provide potential novel approaches to assist with identifying children with bacterial meningitis. FUNDING: This study was funded by the Meningitis Research Foundation, Pfizer and the NIHR Programme Grants for Applied Research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".