Gamma Knife for Cerebral Arteriovenous Malformations at a Single Centre
Bibliographic record
Abstract
BACKGROUND: We report the results of a consecutive series of patients treated with Gamma Knife (GK) Surgery for cerebral arteriovenous malformations (AVMs). METHODS: We retrospectively reviewed 69 patients treated with GK for cerebral AVMs between November 2003 and April 2009, recording clinical data, treatment parameters, and AVM obliteration rates in order to assess our effectiveness with GK in treating these lesions. RESULTS: Ten patients were lost to follow-up. Presentations included: seizure (24), hemorrhage (18), persistent headache (12), progressing neurological signs (10), and incidental (9). In 24 patients (34.8%) treatment planning consisted of digital subtraction angiography (DSA), magnetic resonance imaging (MRI), and computed tomogram (CT) angiography (CTA). Currently we rely predominantly on CTA and/or MRI scanning only. Fourty-one patients have been followed for a minimum of 3 years; average age 40.9 yr., 58.5% males. Average dose at the 50% isodose line was 20.3 Gy (range 16 to 26.4 Gy). Obliteration was observed in 87.8% by MRI, CT, or DSA. Not all obliteration was confirmed by DSA. Complications occurred in 12 of 59 (20.3%) patients, and in 11 of 41 (26.8%) with 3 year follow-up. Major (temporary) complications for the 59 included symptomatic cerebral edema (7), seizure (2), and hemorrhage (1). Major permanent complications occurred in one patient suffering a cranial nerve V deafferentation, and in two patients suffering a hemorrhage. CONCLUSION: GKS for cerebral AVM's offers an effective and safe method of treatment, with low permanent complication rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".