Gamma Knife Radiosurgery for Pediatric Arteriovenous Malformations: A Canadian Experience
Bibliographic record
Abstract
BACKGROUND: Gamma Knife (GK) radiosurgery for pediatric arteriovenous malformations (AVM) of the brain presents a non-invasive treatment option. We report our institutional experience with GK for pediatric AVMs. METHODS: We performed a retrospective review of all pediatric patients treated with GK for cerebral AVMs at our institution from November 2003 up to and including September 2014. Patient demographics, AVM characteristics, treatment parameters and AVM responses were recorded. RESULTS: Nineteen patients were treated, with 4 lost to follow-up. The mean age was 14.2 years (range. 7-18 years), with 10 being males (52.6%). The mean AVM diameter and volume were 2.68 cm and 3.10 cm3 respectively. The mean Spetzler-Martin (SM) and Pollock grades of the treated AVMs were 2.4 and 0.99 respectively. The mean follow-up was 62 months. All AVMs treated demonstrated a response on follow-up imaging. Nine of 15 (60.0%) patients displayed obliteration of their AVMs. Nine of 11 patients with a minimum of 3 years follow-up (81.8%) displayed obliteration, with SM and Pollock grades correlating to the chance of obliteration in this group. Two patients developed post-GK edema requiring short course dexamethasone therapy. No other major complications occurred. No permanent complications occurred. CONCLUSIONS: GK radiosurgery for pediatric AVMs offers a safe and effective treatment option, with low permanent complication rates during early follow-up.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".