Utilizing preprocedural CT scans to identify patients at risk for suboptimal external ventricular drain placement with the freehand insertion technique
Bibliographic record
Abstract
OBJECTIVE: Freehand insertion of external ventricular drains (EVDs) using anatomical landmarks is considered the primary method for placement, although alternative techniques have shown improved accuracy in positioning. The purpose of this study was to retrospectively evaluate which features of the baseline clinical history and preprocedural CT scan predict EVD positioning into suboptimal and unsatisfactory locations when using the freehand insertion technique. METHODS: A retrospective chart review was performed evaluating 189 consecutive adult patients who received an EVD via freehand technique through an anterior burr hole between January 1, 2014, and December 31, 2015, at a Level 1 trauma facility in Edmonton, Alberta, Canada. The primary outcome measures included features associated with suboptimal positioning (Kakarla grade 1 vs Kakarla grades 2 and 3). The secondary outcome measures were features associated with unsatisfactory positioning (Kakarla grades 1 and 2 vs Kakarla grade 3). RESULTS: Fifty-one EVDs (27%) were suboptimally positioned. Fifteen (8%) EVDs were placed into eloquent cortex or nontarget CSF spaces. Admitting diagnosis, head height-to-width ratio in axial plane, and side of predominant pathology were found to be significantly associated with suboptimal placement (p = 0.02, 0.012, and 0.02, respectively). A decreased height-to-width ratio was also associated with placement into only eloquent cortex and/or nontarget CSF spaces (p = 0.003). CONCLUSIONS: Freehand insertion of an EVD is associated with significant suboptimal positioning into parenchyma and nontarget CSF spaces. The likelihood of inaccurate EVD placement can be predicted with baseline clinical and radiographic features. The patient's height-to-width ratio represents a novel potential radiographic predictor for malpositioning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".