P.181 Surgical results with low-grade arteriovenous malformations: a single center 16-year experience
Bibliographic record
Abstract
Background: Advancements in AVM surgical techniques for cerebral arteriovenous malformation (AVM) underscore its efficacy. Our research aims to showcase the positive outcomes of treating low-grade AVMs surgically, focusing on safety and effectiveness. Methods: We retrospectively reviewed 55 patients with Spetzler-Martin (S-M) grade 1 and 2 AVMs who underwent surgical resection between January 2009 and December 2024. Results: In our study, 55 patients with S-M grade 1 and 2 AVMs underwent surgical resection, evenly divided between grades 1 (50.9%) and 2 (49.1%). Intracranial hemorrhage was the primary symptom in 74.5% of cases. Pre-operative Glasgow coma scale (GCS) scores revealed 69.1% of patients scored above 13, with 18% below 8. Successful resection was achieved in 87.3%. Postoperatively, 95.5% of ruptured and 90.9% of unruptured AVM patients showed lower or same modified Rankin scale scores. Poorer outcomes were significantly linked to lower GCS scores and intranidal/flow-related aneurysms. Conclusions: Our findings indicate surgical resection as a beneficial treatment for low-grade AVMs, yielding high cure rates and positive functional outcomes in both ruptured and unruptured cases. Preoperative GCS scores and the presence of associated aneurysms are predictive of postoperative functional status. Additionally, managing postoperative seizures effectively is key to enhancing prognosis
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".