Preoperative or preembolization lesion targeting using rotational angiographic fiducial marking in the neuroendovascular suite
Bibliographic record
Abstract
Three-dimensional rotational digital subtraction (DS) angiography and DynaCT allow precise localization of intracranial arteriovenous fistulas (AVFs) with fiducial markers that have helped in surgical planning. These techniques are particularly useful when the AVF is not evident on cross-sectional imaging. The authors demonstrate the utility of 3D DS angiography and DynaCT in the localization of intracranial AVFs in 3 cases. Their first case was a dural AVF with multiple arterial feeders from the left occipital artery that drained into the left transverse sinus. Blood flow to the left transverse sinus was first decreased by embolizing the branch arterial feeders with polyvinyl alcohol particles. Thereafter, 3D DS angiography enabled precise localization of the site for the bur hole creation with a fiducial to allow access for the transverse sinus in the second part of the procedure where definitive transvenous sinus embolization of the dural AVF with coils was performed. They also used 3D DS angiography and DynaCT with fiducials for precise localization of a superficial pial AVF (Case 2) and a tentorial AVF (Case 3) not visible on cross-sectional angiography. With the precise localization of the target lesion, the neurosurgeons were able to perform relatively small craniotomies, minimizing the cranial opening yet allowing the opening for full access to the lesion. By correlating 3D DS angiography/DynaCT with CT images, the neurosurgeon could use neuronavigation in cases of AVF not appreciated on cross-sectional imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".