Neonatal Paenibacilliosis: <i>Paenibacillus</i> Infection as a Novel Cause of Sepsis in Term Neonates With High Risk of Sequelae in Uganda
Bibliographic record
Abstract
BACKGROUND: Paenibacillus thiaminolyticus may be an underdiagnosed cause of neonatal sepsis. METHODS: We prospectively enrolled a cohort of 800 full-term neonates presenting with a clinical diagnosis of sepsis at 2 Ugandan hospitals. Quantitative polymerase chain reaction specific to P. thiaminolyticus and to the Paenibacillus genus were performed on the blood and cerebrospinal fluid (CSF) of 631 neonates who had both specimen types available. Neonates with Paenibacillus genus or species detected in either specimen type were considered to potentially have paenibacilliosis, (37/631, 6%). We described antenatal, perinatal, and neonatal characteristics, presenting signs, and 12-month developmental outcomes for neonates with paenibacilliosis versus clinical sepsis due to other causes. RESULTS: Median age at presentation was 3 days (interquartile range 1, 7). Fever (92%), irritability (84%), and clinical signs of seizures (51%) were common. Eleven (30%) had an adverse outcome: 5 (14%) neonates died during the first year of life; 5 of 32 (16%) survivors developed postinfectious hydrocephalus (PIH) and 1 (3%) additional survivor had neurodevelopmental impairment without hydrocephalus. CONCLUSIONS: Paenibacillus species was identified in 6% of neonates with signs of sepsis who presented to 2 Ugandan referral hospitals; 70% were P. thiaminolyticus. Improved diagnostics for neonatal sepsis are urgently needed. Optimal antibiotic treatment for this infection is unknown but ampicillin and vancomycin will be ineffective in many cases. These results highlight the need to consider local pathogen prevalence and the possibility of unusual pathogens when determining antibiotic choice for neonatal sepsis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".