F.4 Risk factors for 30-day postoperative infection in pediatric ventricular shunts for hydrocephalus
Bibliographic record
Abstract
Background: Ventricular shunt infections lead to significant morbidity and mortality. This study aimed to identify risk factors for 30-day postoperative infection outcomes of ventricular shunts for pediatric hydrocephalus. Methods: A retrospective cohort study using the National Surgical Quality Improvement Program (NSQIP) Pediatric database for years 2016-2021 was conducted. Patients under 18 years undergoing ventricular shunt surgery were included. The primary outcome was 30-day postoperative shunt infection. A multivariable logistic regression analysis of fourteen prognostic variables was performed. Results: A total of 10,878 patients (mean age 3.1 years, 44.2% female) were included. The 30-day postoperative shunt infection rate was 3.7%. Infection risk increased with nutritional support, longer operating room duration, and congenital hydrocephalus. Risk decreased with increasing age, intraoperative intraventricular antibiotics, and first-time shunt placement. Variables not significantly affecting infection risk included sex, BMI, ostomy, tracheostomy, neuromuscular disease, structural pulmonary/airway abnormality, steroid use, antibiotic-impregnated shunts, and endoscopic catheter placement. Conclusions: Postoperative shunt infections in pediatric patients are influenced by both modifiable and non-modifiable factors. Identifying and addressing modifiable risks can significantly reduce infection rates, minimize the need for surgical revisions, and enhance therapeutic outcomes and overall quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".