An Unusual Cause of Neonatal Infection: A Case Report of <i>Campylobacter coli</i> Meningitis and Sepsis
Bibliographic record
Abstract
Neonatal meningitis is associated with increased morbidity, mortality, and long-term consequences. Despite the use of newer techniques, the diagnosis remains challenging, especially in cases caused by rare pathogens. Campylobacter is widely known as the most common cause of bacterial gastroenteritis. However, invasive infections in neonates have been rarely described in the literature. A rare case of neonatal meningitis caused by Campylobacter coli is presented in this case report. A 14-day-old male and late preterm neonate without a remarkable perinatal history was admitted to our Pediatric Department with a 10-h history of fever and loose stools. The initial laboratory studies suggested the diagnosis of meningitis, but isolating the responsible pathogen in blood and cerebrospinal fluid cultures was demanding. After the cultures were repeated and incubated in microaerophilic conditions, Campylobacter coli was confirmed as the etiological agent. Based on antibiotic susceptibility tests, the neonate had a 21-day course of antibiotic therapy with cefotaxime, a third-generation cephalosporin, and remained healthy during the illness without experiencing any neurological sequelae. This case report highlights that rare pathogens should be considered and searched for in cases of neonatal meningitis when there is no identifiable cause with routine microbiological techniques.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".