P.207 Reducing the risks of proximal and distal shunt failure in adult hydrocephalus: A Shunt Outcomes Quality Improvement (ShOut-QI) Study
Bibliographic record
Abstract
Background: Ventriculoperitoneal (VP) shunt failures in adult patients are common and subject patients to multiple surgeries and a decreased quality of life. A prospective cohort Shunt Outcomes Quality Improvement (ShOut-QI) initiative was implemented to reduce shunt failure incidence through neuronavigation-assisted proximal catheter insertion and laparoscopy-guided distal catheter anchoring over the liver dome to drain CSF away from the omentum. Methods: “Pre-ShOut” and “Post-ShOut” groups of patients included those with and without neuronavigation/laparoscopy, respectively for insertion of a new VP shunt. The primary outcome was shunt failure which was defined as any return to surgery for shunt revision as determined with a standardized clinical and radiology follow-up protocol. Results: 244 patients (97 Pre-ShOut, 147 Post-ShOut), mean age 73 years, were enrolled over a 7-year interval and observed for a mean duration of 4 years after shunt insertion. Neuronavigation improved proximal catheter placement accuracy by 20% (p<.001), and shunt failure occurred in 57% vs 23% in the Pre-ShOut and Post-ShOut groups, respectively (p=.008), representing a 53% relative risk reduction in the incidence of shunt failure. Conclusions: Adult shunt failure incidence may be significantly reduced by improving the accuracy of proximal catheter placement with neuronavigation and reducing the risk of distal catheter failure with laparoscopic-guided placement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".