Intraoperative intraarterial indocyanine green video-angiography for disconnection of a perimedullary arteriovenous fistula: illustrative case
Bibliographic record
Abstract
BACKGROUND: Intraarterial (IA) indocyanine green (ICG) angiography is an intraoperative imaging technique offering special and temporal characterization of vascular lesions with very fast dye clearance. The authors' aim is to demonstrate the use of IA ICG angiography to aid in the surgical treatment of a perimedullary thoracic arteriovenous fistula (AVF) in a hybrid operating room (OR). OBSERVATIONS: A 31-year-old woman with a known history of spinal AVF presented with 6 weeks of lower-extremity weakness, gait imbalance, and bowel/bladder dysfunction. Magnetic resonance imaging revealed an extensive series of flow voids across the thoracic spine, most notably at T11-12. After partial embolization, she was taken for surgical disconnection in a hybrid OR. Intraoperative spinal digital subtraction angiography was performed to identify feeding vessels. When the target arteries were catheterized, 0.05 mg of ICG in 2 mL of saline was injected, and the ICG flow in each artery was recorded using the microscope. With an improved surgical understanding of the contributing feeding arteries, the authors achieved complete in situ disconnection of the AVF. LESSONS: IA ICG angiography can be used in hybrid OR settings to illustrate the vascular anatomy of multifeeder perimedullary AVFs and confirm its postoperative disconnection with a fast dye clearance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| 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".