Distinguishing community-acquired bacterial and viral meningitis: Microbes and biomarkers
Bibliographic record
Abstract
Diagnostic tools to differentiate between community-acquired bacterial and viral meningitis are essential to target the potentially lifesaving antibiotic treatment to those at greatest risk and concurrently spare patients with viral meningitis from the disadvantages of antibiotics. In addition, excluding bacterial meningitis and thus decreasing antibiotic consumption would be important to help reduce antimicrobial resistance and healthcare expenses. The available diagnostic laboratory tests for differentiating bacterial and viral meningitis can be divided microbiological pathogen-focussed methods and biomarkers of the host response. Bacterial culture-independent microbiological methods, such as highly multiplexed nucleic acid amplification tests, are rapidly making their way into the clinical practice. At the same time, more conventional host protein biomarkers, such as procalcitonin and C-reactive protein, are supplemented by newer proteomic and transcriptomic signatures. This review aims to summarise the current state and the recent advances in diagnostic methods to differentiate bacterial from viral meningitis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".