The Utility of Superselective Rotational Angiography for Frameless Stereotactic Navigation During Craniotomy for Micro-Arteriovenous Malformation
Bibliographic record
Abstract
BACKGROUND: Micro-arteriovenous malformations (AVMs) can present challenges to neurosurgeons with respect to localization during resection. We sought to describe a novel method that merges super-selective 3-dimensional angiographic images with magnetic resonance imaging (MRI) sequences to facilitate frameless stereotaxic navigation during AVM surgery. METHODS: A retrospective analysis was performed comprising cases that employed merging of angiographic and MRI images for navigation purposes. Baseline clinical and imaging features were recorded. The technique and operative experiences were analyzed descriptively and presented alongside detailed illustrative cases. RESULTS: During the review period, 11 cases were identified where this technique was employed. Successful image acquisition and merging was possible in all cases. Complete obliteration of the target pathology was achieved in all cases. Precise localization of the micro-AVMs minimized dissection in eloquent cortex. CONCLUSIONS: Superselective 3-dimensional angiographic images merged to baseline MRI sequences facilitates planning and navigation during surgery for micro-AVMs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".