Practice Patterns of Canadian Neurosurgeons in Laparoscopic-Assisted Placement of Ventriculoperitoneal Shunts
Bibliographic record
Abstract
BACKGROUND: Cerebrospinal fluid diversion via ventriculoperitoneal (VP) shunting is the mainstay treatment for hydrocephalus. Traditionally, neurosurgeons place the abdominal catheter through abdominal incision, which has become an efficient and standardized technique. This approach carries a 10-30% complication rate, including infection, catheter obstruction, misplacement, hemorrhage and post-operative pain. Laparoscopic assistance (LA) is an emerging alternative to mini-laparotomy, with potential benefits including reduced distal catheter malplacement and shorter operative times. Most investigations on LA are limited to single centers, with no data from Canada. This study aims to identify practice patterns in VP shunting within a Canadian context. METHODS: Practicing Canadian neurosurgeons were surveyed using a modified Delphi methodology. The survey was distributed to practicing neurosurgeons via the Canadian Neurosurgical Society and the Canadian Neurosurgery Research Collaborative. RESULTS: Across two rounds, 36 neurosurgeons participated, representing all provinces with academic neurosurgical centers. Consensus was reached on five out of eight topics. Findings revealed that 65.5% of respondents had experience with LA, and 93% believed it reduced distal catheter malposition. Infection (77.8%), distal catheter obstruction (82.9%) and proximal obstruction (69.4%) were identified as the most common complications, each occurring in up to 10% of cases. In total, 71% anticipated eventual reduced operative times with increased LA experience. CONCLUSION: Canadian neurosurgeons did not identify major barriers to LA beyond personal preference. LA is expected to improve distal catheter placement, though its broader benefits remain uncertain. Patient comorbidities were considered a greater risk factor for complications than surgical technique alone.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".