Cerebrospinal fluid shunt infections in children
Bibliographic record
Abstract
Cerebrospinal fluid (CSF) shunts are commonly used for the long-term management of hydrocephalus in children. Shunt infection remains a common complication, occurring in about 5%–15% of CSF shunts. This narrative review summarises key evidence from recent literature on the epidemiology, pathogenesis, clinical presentation, diagnosis, management, outcomes and prevention of CSF shunt infections in children. The majority of shunt infections occur due to contamination at the time of surgery, with coagulase-negative staphylococci andStaphylococcus aureusbeing the most common infecting organisms. Clinical presentations of shunt infection can be varied and difficult to recognise. CSF cultures are the primary test used for diagnosis. Other CSF and blood parameters may aid in diagnosis but lack sensitivity and specificity. Core aspects of management of shunt infections include systemic antimicrobial therapy and surgical removal of the shunt. However, many specific treatment recommendations are limited by a lack of robust evidence from large studies or controlled trials. Shunt infections may result in long hospital stays, worsening hydrocephalus, neurological sequelae and other complications, as well as death. Therefore, reducing the incidence of infection and optimising management are high priorities. Antibiotic prophylaxis at the time of shunt placement, improved surgical protocols and antibiotic-impregnated shunts are key strategies to prevent shunt infections. Nevertheless, further work is needed to identify additional strategies to prevent complications and improve outcomes.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".