Neonatal Citrobacter koseri meningitis and brain abscess
Bibliographic record
Abstract
Citrobacter koseri meningitis is a very rare cause of neonatal meningitis. It is characterized by serious complications like cerebral abscesses and high mortality. This is the first neonatal case of cerebral abscesses in UK following C. koseri meningitis, who survived with no serious neurological deficit. Serial neuroimaging is the key to diagnose cerebral complications early. Neonatal meningitis is a well known serious clinical condition associated with significant morbidity and mortality12 which can be complicated by brain abscess formation and ventriculitis.3456 Although there has been a decrease in the overall mortality in the last decade attributable to improved supportive care and the use of more efficacious antibiotics such as third generation cephalosprorins,7 the incidence and morbidity attributed to this condition has remained largely unchanged.12 Up to forty-five percent of neonatal meningitis is caused by Gram negative bacilli, 123 and is associated with high morbidity7 and mortality of about eighty percent.4 Neonatal meningitis caused by Citrobacter koseri is extremely rare and is often complicated by brain abscess and ventriculitis, with only a few cases reported from the USA,345 India,8 Brazil,9 Israel,10 and Canada.11 In the UK, there have been ten reported cases of Citrobacter meningitis, 12131415 nine of which were C. koseri and one C. freundii.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".