Hemorrhage risk of cerebral dural arteriovenous fistulas following Gamma Knife radiosurgery in a multicenter international consortium
Bibliographic record
Abstract
OBJECTIVE: The authors performed a study to evaluate the hemorrhagic rates of cerebral dural arteriovenous fistulas (dAVFs) and the risk factors of hemorrhage following Gamma Knife radiosurgery (GKRS). METHODS: Data from a cohort of patients undergoing GKRS for cerebral dAVFs were compiled from the International Radiosurgery Research Foundation. The annual posttreatment hemorrhage rate was calculated as the number of hemorrhages divided by the patient-years at risk. Risk factors for dAVF hemorrhage prior to GKRS and during the latency period after radiosurgery were evaluated in a multivariate analysis. RESULTS: A total of 147 patients with dAVFs were treated with GKRS. Thirty-six patients (24.5%) presented with hemorrhage. dAVFs that had any cortical venous drainage (CVD) (OR = 3.8, p = 0.003) or convexity or torcula location (OR = 3.3, p = 0.017) were more likely to present with hemorrhage in multivariate analysis. Half of the patients had prior treatment (49.7%). Post-GRKS hemorrhage occurred in 4 patients, with an overall annual risk of 0.84% during the latency period. The annual risks of post-GKRS hemorrhage for Borden type 2-3 dAVFs and Borden type 2-3 hemorrhagic dAVFs were 1.45% and 0.93%, respectively. No hemorrhage occurred after radiological confirmation of obliteration. Independent predictors of hemorrhage following GKRS included nonhemorrhagic neural deficit presentation (HR = 21.6, p = 0.027) and increasing number of past endovascular treatments (HR = 1.81, p = 0.036). CONCLUSIONS: Patients have similar rates of hemorrhage before and after radiosurgery until obliteration is achieved. dAVFs that have any CVD or are located in the convexity or torcula were more likely to present with hemorrhage. Patients presenting with nonhemorrhagic neural deficits and a history of endovascular treatments had higher risks of post-GKRS hemorrhage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".