Abstract 22: How Are We Doing At Treating Arteriovenous Malformations?
Bibliographic record
Abstract
Background: Arteriovenous malformations (AVMs) are incompletely understood complex vascular lesions that can cause significant deformity and morbidity. This study reviews our experience with AVMs at a quaternary care teaching hospital. Methods: All patients treated for AVMs in our vascular anomaly clinic from 1991 to 2018 were reviewed. Data extraction included demographics, clinical presentation and course, radiology reports, and treatments. Progression was defined as advancement to a higher Schobinger stage (1 through 4) before treatment. Recurrence was defined as expansion following embolization or resection. Results: 58 patients met inclusion criteria. 60.3% lesions were located in the head and neck, 10.3% on the trunk, and 29.3% on the extremities. 60% patients were female, and average follow up time was 2.9 years. 18.9% patients presented at Stage 1, 54.7% at Stage 2, 24.5% at Stage 3, and 1.9% at Stage 4. 98% of lesions progressed to a higher Schobinger stage without intervention. 124 interventions, including surgery and embolization, were performed on our cohort. Patients treated with embolization alone had a per treatment recurrence rate of 59%, and patients treated with surgical resection had a per treatment recurrence rate of 33%. Advanced lesions had higher rates of recurrence (Stage 2 60%, Stage 3 83%, Stage 4 100%). More advanced lesions also recurred more quickly. 3 lesions treated at Stage 1 did not show evidence of recurrence. Conclusions: AVMs ultimately progress without intervention. We are not doing well at treating later stage AVMs as these lesions require a tremendous amount of resources and have a high rate of recurrence despite treatment. Our data suggests surgery had lower recurrences versus embolization alone. Treatment of lower-staged lesions may provide longer recurrence-free disease control.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".