Cerebrospinal Fluid Shunt Infections: A Multicenter Pediatric Study
Bibliographic record
Abstract
BACKGROUND: Infections complicate 5%-10% of cerebrospinal fluid (CSF) shunts. We aimed to describe the characteristics and contemporary pathogens of shunt infections in children in Canada and the United States. METHODS: Descriptive case series at tertiary care hospitals in Canada (N = 8) and the United States (N = 3) of children up to 18 years of age with CSF shunt infections from July 1, 2013, through June 30, 2019. RESULTS: There were 154 children (43% female, median age 2.7 years, 50% premature) with ≥1 CSF shunt infections. Median time between shunt placement and infection was 54 days (interquartile range, 24 days-2.3 years). Common pathogens were coagulase-negative staphylococci (N = 42; 28%), methicillin-susceptible Staphylococcus aureus (N = 24; 16%), methicillin-resistant S. aureus (N = 9; 5.9%), Pseudomonas aeruginosa (N = 9; 5.9%) and other Gram-negative bacilli (N = 14; 9.0%). Significant differences between pathogens were observed, including timing of infection (P = 0.023) and CSF leukocyte count (P = 0.0019); however, differences were not sufficient to reliably predict the causative organism based on the timing of infection or discriminate P. aeruginosa from other pathogens based on clinical features. Empiric antibiotic regimens, which included vancomycin (71%), cefotaxime or ceftriaxone (29%) and antipseudomonal beta-lactams (33%), were discordant with the pathogen isolated in five cases. There was variability between sites in the distribution of pathogens and choice of empiric antibiotics. Nine children died; 4 (44%) deaths were attributed to shunt infection. CONCLUSIONS: Staphylococci remain the most common cause of CSF shunt infections, although antibiotic resistant Gram-negative bacilli occur and cannot be reliably predicted based on clinical characteristics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".