A Multicenter Multinational Registry for Assessing Ventriculoperitoneal Shunt Infections for Hydrocephalus
Bibliographic record
Abstract
BACKGROUND: Reported infection rates after ventriculoperitoneal shunt surgery vary from 1 to 25%. Antibiotic-impregnated (AI) catheters may reduce shunt infection rates, but this is uncertain. OBJECTIVE: To establish a prospective shunt registry to evaluate short-term (3-month) infection rates associated with ventriculoperitoneal shunts and standard or AI catheters during surgical treatment of hydrocephalus. METHODS: A prospective, multicenter, noncontrolled, open-label registry investigated patients with de novo catheter implantation or catheter replacement of an existing ventriculoperitoneal shunt. The primary outcome was shunt infection. RESULTS: A total of 440 patients were entered into the registry at 10 sites: 3 in North America, 2 in Singapore, 4 in China and 1 in India. Seven patients were excluded. Of the 433 remaining patients, 314 had new shunts and 119 were revisions. Shunt infections occurred in 14 of 433 patients (3.2%) overall and in 2 of 37 infants (5.2%) younger than 1 year. AI catheters were used in 46 of 433 patients at 7 centers. The shunt infection rate was 0 of 46 for shunts with AI catheters and 14 of 387 (3.6%) without AI catheters. Infection rates were similar with AI catheters, adjusting for age and catheter type. CONCLUSION: The overall shunt infection rate was lower than in previous multicentered studies. The low infection rate and low rate of AI catheter use precludes any meaningful statement regarding the value of AI catheters in reducing the infection rate. Consideration should be given to performing a well designed, adequately powered, prospective randomized controlled trial to determine whether AI catheters reduce shunt infection rates.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".