Culture-negative bacteria: a blind spot in bacterial pathogen prioritization
Bibliographic record
Abstract
Culture-negative bacteria (CNB), including Bartonella spp., Coxiella burnetii, Rickettsia spp., Orientia tsutsugamushi, and Leptospira spp., are frequent yet underrecognized causes of febrile illness in low- and middle-income countries (LMICs). Although these pathogens cause significant morbidity and mortality, they often remain undetected because standard culture techniques fail to identify them, resulting in systematic underdiagnosis. This narrative review examines existing criteria used in infectious disease guidelines for pathogen recognition and prioritization, applying them to CNB to assess how these organisms fit, or fail to fit, within current frameworks. We discuss the diagnostic limitations that impede CNB detection, as well as the cognitive biases that lead clinicians and public health practitioners to overlook these infections. The disproportionate impact of CNB in LMICs, where diagnostic infrastructure is limited and pathogen diversity is high, further fragments epidemiological data and constrains research investment. Collectively, these factors perpetuate the neglect of CNB in clinical practice, public health policy, and global health research agendas.
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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 | 0.054 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".