Benefit of Advanced 3D DSA and MRI/CT Fusion in Neurovascular Pathology
Bibliographic record
Abstract
Digital subtraction angiography provides excellent spatial and temporal resolution; however, it lacks the capability to depict the nonvascular anatomy of the brain and spinal cord.A review of the institutional database identified five patients in whom a new integrated fusion workflow of cross-sectional imaging and 3D rotational angiography (3DRA) provided important diagnostic information and assisted in treatment planning. These included two acutely ruptured brain arteriovenous malformations (AVM), a small superficial brainstem AVM after radiosurgery, a thalamic microaneurysm, and a spine AVM, and fusion was crucial for diagnosis and influenced further treatment.Fusion of 3DRA and cross-sectional imaging may help to gain a deeper understanding of neurovascular diseases. This is advantageous for planning and providing treatment and, most importantly, may harbor the potential to minimize complication rates. Integrating image fusion in the work-up of cerebrovascular diseases is likely to have a major impact on the neurovascular field in the future.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".