P.125 Cerebral AVM recurrence post gamma knife obliteration: a 20 year single centre retrospective analysis and review of literature
Bibliographic record
Abstract
Background: Pediatric arteriovenous malformations (AVMs) are rare, but represent the leading cause of intracranial hemorrhage in children. These are traditionally understood to be congenital lesions, however AVMs recurrence within previously unaffected tissue challenges this understanding. Recurrence after microsurgery and endovascular treatment have been studied in greater detail, but little literature exists surrounding recurrence after Gamma Knife Radiosurgery (GKRS). Methods: We performed a retrospective chart review of all pediatric AVMs treated with GKRS at our centre. Charts were assessed by two reviewers to identify cases of AVM recurrence after angiographically confirmed obliteration. To contextualize our institutional patterns, we also performed a structured literature review of published data reporting pediatric AVM recurrence after GKRS. Results: Our institutional review revealed two cases of AVM recurrence after angiographically proven cure, and our review of literature identified nine retrospective reviews and three case reports, which in total reported 22 individual cases of recurrence. The recurrence rate in the retrospective reviews ranged from 0 to 18%. Conclusions: The current work illustrates that while AVM recurrence is rare, it is a possible complication of GKRS. There was also a qualitative suggestion that embolization prior to CT increased risk of recurrence. Both these facts should be included in decision-making and patient counselling.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".