P.166 Assessment of External Ventricular Catheter Associated Infections at the Ottawa Hospital: Literature Review and Quality Improvement Initiative
Bibliographic record
Abstract
Background: External ventricular drains are a lifesaving intervention in the management of acute hydrocephalus. EVD associated infections vary significantly, and expert panels recommend reviewing institutional policies if infection rates exceed 10%. The audit aims to identify the infection rate at our institution, whereas the literature review aims to synthesize a new institutional EVD best practice guideline. Methods: An audit of EVD catheters inserted in the time period between 07/01/2019 and 10/25/2020 was conducted. Statistical analysis to calculate absolute incidence, infections per 1000 days of catheter use. A literature review to identify best practices for the insertion and management of EVDs was conducted. Results: 75 patients required a total of 105 EVD catheters. There were 16 (15.3%) EVD related infections, equating to 14.3 infections per 1000 days. Fifty percent of patients developed an EVD related infection within 9 days of insertion. Most infections were induced by skin flora (87.5%). A comprehensive step-by-step EVD insertion and management protocol was developed aiming to reduce the risk of infection. Conclusions: The incidence of EVD associated infections at the Ottawa Hospital is significantly higher than acceptable rates as suggested by expert panels. A new evidence-based best practice guidelines should be implemented. A follow-up audit is necessary.
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.032 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".