Risk Factors for First Cerebrospinal Fluid Shunt Infection: Findings from a Multi-Center Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To quantify the extent to which cerebrospinal fluid (CSF) shunt revisions are associated with increased risk of CSF shunt infection, after adjusting for patient factors that may contribute to infection risk. STUDY DESIGN: We used the Hydrocephalus Clinical Research Network registry to assemble a large prospective 6-center cohort of 1036 children undergoing initial CSF shunt placement between April 2008 and January 2012. The primary outcome of interest was first CSF shunt infection. Data for initial CSF shunt placement and all subsequent CSF shunt revisions prior to first CSF shunt infection, where applicable, were obtained. The risk of first infection was estimated using a multivariable Cox proportional hazard model accounting for patient characteristics and CSF shunt revisions, and is reported using hazard ratios (HRs) with 95% CI. RESULTS: Of the 102 children who developed first infection within 12 months of placement, 33 (32%) followed one or more CSF shunt revisions. Baseline factors independently associated with risk of first infection included: gastrostomy tube (HR 2.0, 95% CI, 1.1, 3.3), age 6-12 months (HR 0.3, 95% CI, 0.1, 0.8), and prior neurosurgery (HR 0.4, 95% CI, 0.2, 0.9). After controlling for baseline factors, infection risk was most significantly associated with the need for revision (1 revision vs none, HR 3.9, 95% CI, 2.2, 6.5; ≥2 revisions, HR 13.0, 95% CI, 6.5, 24.9). CONCLUSIONS: This study quantifies the elevated risk of infection associated with shunt revisions observed in clinical practice. To reduce risk of infection risk, further work should optimize revision procedures.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".