Safety and Efficacy of Gamma Knife Radiosurgery for the Management of Koos Grade 4 Vestibular Schwannomas
Bibliographic record
Abstract
BACKGROUND: Gamma Knife radiosurgery (GKRS) is commonly used in treating small vestibular schwannomas; however, its use for larger vestibular schwannomas is still controversial. OBJECTIVE: To assess the long-term safety and efficacy of treating eligible Koos grade 4 vestibular schwannomas with GKRS. METHODS: We conducted a single-center, retrospective evaluation of patient undergoing GKRS for Koos grade 4 vestibular schwannomas. We evaluated clinical, imaging, and treatment characteristics and assessed treatment outcome. Inclusion criteria were tumor size of ≥4 cm and follow-up of at least 6 months. Patients with neurofibromatosis type 2 were excluded. Primary outcomes measured were tumor control rate, hearing and facial function preservation rate, and complications. All possible factors were analyzed to assess clinical significance. RESULTS: Sixty-eight patients met inclusion criteria. Median follow-up was 47 months (range, 6-125 months). Baseline hearing was serviceable in 60%. Median tumor volume at radiosurgery was 7.4 cm (range, 4-19 cm). The median marginal dose used was 12 Gy at the 50% isodose line. Actuarial tumor control rates were 95% and 92% at 2 and 10 years, respectively. Actuarial serviceable hearing preservation rates were 89% and 49% at 2 and 5 years, respectively. Facial nerve preservation was 100%. Clinical complications included balance disturbance (11%), facial pain (10%), facial numbness (5%), and tinnitus (10%). Most complications were mild and transient. Hydrocephalus occurred in 3 patients, requiring ventriculoperitoneal shunt insertion. Larger tumor size was significantly associated with persisting symptoms post-treatment. CONCLUSION: Patients with Koos grade 4 vestibular schwannomas and minimal symptoms can be treated safely and effectively with GKRS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".