Classification, angioarchitecture and treatment outcomes of medullary bridging vein-draining dural arteriovenous fistulas in the foramen magnum region: a multicenter study
Bibliographic record
Abstract
PURPOSE: This study aimed to classify medullary bridging vein-draining dural arteriovenous fistulas (MBV-DAVFs) located around the foramen magnum (FM) according to their location and characterize their angioarchitecture and treatment outcomes. METHODS: Patients with MBV-DAVFs diagnosed between January 2013 and October 2022 were included. MBV-DAVFs were classified into four groups. Jugular vein-bridging vein (JV-BV) DAVF: located in proximity to jugular fossa, Anterior condylar vein (ACV)-BV DAVF: proximity to anterior condylar canal, Marginal sinus (MS)-BV DAVF: lateral surface of FM and Suboccipital cavernous sinus (SCS)-BV DAVF: proximity to dural penetration of vertebral artery. RESULTS: Twenty patients were included, three JV-BV, four ACV-BV, three MS-BV and ten SCS-BV DAVFs, respectively. All groups showed male predominance. There were significant differences in main feeders between JV (jugular branch of ascending pharyngeal artery) and SCS group (C1 dural branch). Pial feeders from anterior spinal artery (ASA) or lateral spinal artery (LSA) were visualized in four SCS and one MS group. Drainage pattern did not differ between groups. Transarterial embolization (TAE) was performed in three, two, one and two cases and complete obliteration was obtained in 100%, 50%, 100% and 0% in JV, ACS, MS and SCS group, respectively. Successful interventions without major complications were finally obtained in 100%, 75%, 100%, and 40% in JV, ACS, MS and SCS group, respectively. CONCLUSION: JV-BV DAVFs were successfully treated using TAE alone. SCS-BV DAVFs were mainly fed by small C1 dural branches of vertebral artery often with pial feeders from ASA or LSA, and difficultly treated by TAE alone.
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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 | 0.002 |
| 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 | 0.000 |
| 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".