C.06 A nation-wide prospective multi-centre study of external ventricular drainage accuracy, safety and related complications
Bibliographic record
Abstract
Background: Insertion of an external ventricular drain (EVD) is performed to treat elevated intracranial pressure. EVD catheters are associated with complications such as EVD catheter infection (ECI), intracranial hemorrhage (ICH) and suboptimal catheter placement. As part of the Canadian Neurosurgery Research Collaborative, we sought to investigate the national rate of such complications and their risk factors. Methods: Prospective study of 273 patients from eight academic Canadian neurosurgery centres Results: Infection rate was 6% and predicted by smaller incisions and not peri-procedure antibiotics, tunneling distance, type of antiseptic used or catheter flushing (p>0.05). The mean duration of EVD was 17.7±3.7 in ECI and ventriculitis group which was significantly higher than in patients without ECI (9.4±8.1) (p=0.045). Although the risk of developing ICH was 9.3%, symptomatic ICH was rare. Pre-procedure pharmacological DVT prophylaxis predicted EVD-related ICH(OR 4.73). The rate of suboptimal catheter location was 31% and predicted by the number of passes (p=0.02), but not image guidance, level of training or catheter placement in an operating room setting (p>0.05). Conclusions: This study reports EVD complication rates and their associated risk factors observed within an academic, multicentre Canadian cohort. This information will help to identify strategies to increase the safety of this common neurosurgical procedure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".