Embolisation of Small (< 3 cm) Brain Arteriovenous Malformations
Bibliographic record
Abstract
SUMMARY: The role of embolisation in the treatment of small < 3cm) brain arteriovenous malformations (AVMs) has not been elucidated. We reviewed our experience using embolisation in the treatment of small AVMs and correlated a proposed grading system based on the angioarchitecture to the percentage obliteration achieved by embolisation. Eighty-one small AVMs in 80 patients were embolised from 1984 to 1999. The age range was from 3 to 72 years. The AVMs were given a score from 0 to 6 based on the angioarchitecture. The assigned scores were as follows: nidus (fistula = 0, < 1 cm = 1, 1-3 cm = 2), type offeeding arteries (cortical = 0, perforator or choroidal = 1), number of feeding arteries (single = 0, multiple = 2) and number of draining veins (single = 0, multiple = 1). Angiographic results based on percentage obliteration were grouped into three categories: complete, 66-99%, and 0-65%. The goal of embolisation was cure in 27 AVMs, pre-surgical in 23, pre-radiosurgery in 26, and elimination of an aneurysm in five. Embolisation achieved complete obliteration in 22 (27%) of the 81 AVMs. In the AVMs where the goal was cure, 19 (70%) of 27 were completely obliterated. In the AVMs with angioarchitecture scores of 0-2, 12 (86%) of 14 were cured, with scores of 3-4, 8 (34%) of 24 were cured and with scores of 5-6, 2 (4%) of 44 were cured. Embolisation resulted in transient morbidity of 5.0%, permanent morbidity of 2.5%, and mortality of 1.2%. There were no complications in AVMs with scores of 0-2. Embolisation is an effective treatment of small AVMs when the angioarchitecture is favourable (scores 0-2). This includes pure fistulas and AVMs with a single, pial, feeding artery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".