International Multicenter Survey of Perioperative Management of External Ventricular Drains: Results of the EVD Aware Study
Bibliographic record
Abstract
INTRODUCTION: The perioperative management of patients with external ventricular drains (EVDs) is not well defined, and adherence to published management guidelines unknown. This study investigates practice, patterns, and variability in the perioperative management of patients with EVDs. METHODS: A 31-question survey was sent to 1830 anesthesiologists from 27 institutions in North America, Europe, and Asia. A perioperative EVD Guideline Adherence Score was calculated for the preoperative, transport and intraoperative periods. Differences in management practices between neuroanesthesiologists and non-neuroanesthesiologists, and factors affecting EVD guideline adherence, were examined using bivariate significance tests and linear regression. RESULTS: Among a sample of 599 anesthesiologists (survey response rate, 32.7%), compared with non-neuroanesthesiologists, neuroanesthesiologists were more likely to include baseline neurological examination (P=0.023), hourly cerebrospinal fluid output (P=0.006) and color (P<0.001), intracranial pressure trends (P<0.001), and EVD clamp trial (P<0.001) data in their routine preanesthetic assessment of patients with EVDs. There was a low prevalence of routine intracranial pressure monitoring during patient transport of patients with EVDs (14.4%). Overall, 25.9% of respondents were aware of EVD guidelines, and 21% reported receiving formal training in EVD management. The EVD Guideline Adherence Score was highest among anesthesiologists who reported being very comfortable in managing patients with EVDs compared with those who reported being uncomfortable (9.93 vs. 6.93, P<0.001). CONCLUSIONS: The EVD Aware study identifies opportunities for improvement in the perioperative management of patients with EVDS, including global awareness, formal EVD training, and dissemination of educational tools.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".