Complex shunt system comparison: an observational study by the Hydrocephalus Clinical Research Network
Bibliographic record
Abstract
OBJECTIVE: Treatment of multiloculated hydrocephalus can require multiple procedures and shunt catheters. This study aimed to determine whether there are differences in shunt survival or complications in patients with complex shunt systems based on whether they have separate systems or multiple intracranial catheters with a single distal catheter and a Y- or T-connector. METHODS: The authors retrospectively reviewed the Hydrocephalus Clinical Research Network (HCRN) Core Data Project, a prospective multicenter registry. Patients undergoing first-time placement of a complex ventriculoperitoneal shunt or first-time conversion to a complex shunt were identified and included in the analysis. Propensity-weighted Cox regression was used to control for HCRN center and etiology using the rate of shunt failure (shunt malfunction or shunt infection) as the primary outcome. The final regression model was also adjusted for age and complex chronic conditions. RESULTS: In total, 369 patients were included. One hundred fifty-one patients had separate systems and 218 had Y/T-connectors. After adjustment for age and comorbidities, the rate of shunt failure for systems with Y/T-connectors was not significantly different than that for separate shunt systems: 62% versus 55% (HR 1.20, 95% CI 0.91-1.59, p = 0.197). There was a statistically significant difference in operative time with separate systems having shorter operative times (mean time 63.0 vs 80.0 minutes; mean difference 16.32, 95% CI 7.53-25.10, p < 0.001). CONCLUSIONS: There were no differences in the shunt failure rates or complications between the separate shunt systems and Y/T-connector systems used to treat complex shunts. However, surgical time was significantly shorter with separate shunt systems. These findings suggest that surgeons can tailor the shunt system on the basis of individual patient characteristics.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".