Gamma Knife radiosurgery for the treatment of intracranial dural arteriovenous fistulas
Bibliographic record
Abstract
Background Intracranial dural arteriovenous fistulae (DAVF) may present a treatment challenge. Endovascular embolization is in most cases the first line of treatment but does not always achieve cure. Gamma Knife (GK) radiosurgery represents an alternative treatment option, and the purpose of this study was to further evaluate its utility. Methods We reviewed all cases of DAVF treated between 2009 and 2016 at our institution with GK radiosurgery independently, or following failed/refused endovascular or surgical management. Patients’ clinical files, radiological images, catheter angiograms, and surgical DAVF disconnection reports were retrospectively reviewed. Results Sixteen DAVF (14 patients) treated by GK radiosurgery were identified. Eleven fistulae were aggressive and five were benign. Marginal doses ranged from 15 to 25 Gy. Target volumes ranged from 0.04 to 4.47 cm 3 . In all symptomatic patients, GK treatment resulted in symptom palliation. In 13/15 lesions, cure of symptoms (86.0%) was reported. One lesion was asymptomatic. Angiographic cure was achieved in eight cases (50%), small residual DAVF occurred in four, and four were unchanged. One patient developed headache that resolved at one year. No hemorrhage occurred during the follow-up period. There was no significant association between Borden type and cure rate. Prior failed endovascular treatment and small target volume were associated with lower rates of cure. Conclusions Stereotactic radiosurgery is viable treatment for DAVF. It is very effective in palliating symptoms as a de novo approach or adjunctive to endovascular therapy. In our experience it is only somewhat effective in achieving complete angiographic cure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".